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correct_foundationPlace_00033	FactBench	1	54	https://adtmag.com/articles/2015/02/10/marklogic-nosql-update.aspx	en	MarkLogic Cites Developer Benefits in NoSQL Upgrade	https://adtmag.com/-/med…SquareBlocks.jpg	https://adtmag.com/-/med…SquareBlocks.jpg	[ "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dlead_t1%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=728x90\|700x90\|970x250&tile=1&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dtick_t1%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=974x32&tile=2&c=123456789", "https://adtmag.com/articles/2015/02/10/~/media/ECG/shared/img/socialmedia/linkedin_light.ashx", "https://adtmag.com/articles/2015/02/10/~/media/ECG/shared/img/socialmedia/twitter_x_light.ashx", "https://adtmag.com/~/media/ECG/adtmag/adtmaglogo.svg", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dwallpaper_left%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=152x500\|152x600&tile=3&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dwallpaper_right%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=152x500\|152x600&tile=4&c=123456789", "https://adtmag.com/articles/2015/02/10/~/media/ECG/VirtualizationReview/Images/introimages2014/GEN2GrayStoneSquareBlocks.jpg", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dbox_c1%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=300x250&tile=5&c=123456789", "https://adtmag.com/articles/2015/02/10/-/media/ECG/redmondmag/Images/IntroImagesBigSmall/ToolboxSmall.jpg", "https://adtmag.com/articles/2015/02/10/-/media/General-Images-Unclaimed/CoffeeCup1.jpg", "https://adtmag.com/articles/2015/02/10/-/media/ECG/redmondmag/Images/IntroImagesBigSmall/WeirdSpaceImageSmall.jpg", "https://adtmag.com/articles/2015/02/10/-/media/ECG/adtmag/Images/IntroImages2018/aaa-Big-AI-for-ADTmag.jpg", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dmobileflyout%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=300x250&tile=6&c=123456789", "https://adtmag.com/Captcha.ashx?id=896615F8D9DC4235945A52CEDF55F6C0", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dbox_r1%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=300x250\|300x600\|1x1&tile=7&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dbox_r2%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=300x250\|300x600\|1x1&tile=8&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dbox_r3%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=300x250\|300x600&tile=9&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dlead_t2%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=728x90\|700x90\|970x250&tile=10&c=123456789", "https://adtmag.com/articles/2015/02/10/~/media/ECG/Converge360/design/1105companylogo.png", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dboot_desktop%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=250x90\|1x1&tile=11&c=123456789", "https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.adt&t=item%253db006b27d_d36a_46b9_bd96_bf1ad5902bd2%26pos%253dboot_mobile%26Topic%253dData_Development%252cJavascript%252cDatabases%252cWeb_Development%252cDevelopment&sz=1024x90\|1x1&tile=12&c=123456789" ]	[]	[]	[ "marklogic", "javascript", "java" ]	null	[ "About the Author", "David Ramel" ]	2015-02-10T00:00:00	Enterprise NoSQL database platform vendor MarkLogic Corp. cited "massive enhancements for developers" in the latest version of its flagship database.	en	/design/ECG/adtmag/img/favicon.ico?v=1	ADTmag	https://adtmag.com/Articles/2015/02/10/marklogic-nosql-update.aspx	Enterprise NoSQL database platform vendor MarkLogic Corp. cited "massive enhancements for developers" in the latest version of its flagship database. MarkLogic 8 combines the advantages of NoSQL data stores -- commonly associated with Big Data analytics -- with relational features found in more traditional RDBMS systems, the company said. It further differentiates its NoSQL offering with built-in capabilities for advanced queries, search, semantics and other operational and transactional functionality. In courting developers, MarkLogic cited the heavy use of JavaScript, which now can be used on the server via an embedded runtime using Google's V8 engine or in the middle tier with a new Node.js driver for non-blocking, asynchronous input/output. "The API provides an asynchronous JavaScript interface for key MarkLogic capabilities, such as search, document management, batch loading, transactions, aggregates and alerting," the company said. "Combined with MarkLogic's ability to index and manage JSON documents natively, the Node.js Client API is an ideal tool for full-stack JavaScript development." Of course, developers can take advantage of other programming languages also, and can now work with a new Java Client API providing out-of-the-box data management, query capabilities, aggregation and alerting, the company said. Also highlighting the new release are bitemporal capabilities, which allow rewinding through time-stamped files to discover changes, functionality useful for companies to meet regulatory and compliance requirements. And new semantics improvements help users automatically discover facts and relationships hidden within billions of triples and documents through the use of new inference capability and expanded support of the SPARQL 1.1 query language. Finally, the company touted "a robust Management REST API, enhanced Flexible Replication, simpler out-of-the-box experience and faster backup" in its new release. MarkLogic, noting that it has always had a free developer license, announced a limited offering of a free, one-year developer and production license for new customers running the database on the Amazon Web Services Inc. (AWS) cloud platform. "MarkLogic has been a strategic partner of American Psychological Association (APA) since 2006, helping us to drive revenue with improved data quality, time to market, performance and customer experience," the company quoted customer Beverly Jamison as saying. "Our testing has found that MarkLogic 8's JavaScript and native JSON support will allow us to quickly create even more advanced applications that our members will embrace, and the enhanced semantics will add another layer of search- and presentation-intelligence that will help us maintain our competitive advantage."
correct_foundationPlace_00033	FactBench	1	0	https://help.marklogic.com/News/NewsItem/View/309/marklogic-corporation-establishes-leadership-in-rapidly-evolving-operational-database-market	en	MarkLogic Corporation Establishes Leadership in Rapidly Evolving Operational Database Market	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://help.marklogic.com/__swift/themes/client/images/ml-loader.gif", "https://help.marklogic.com/Base/StaffProfile/DisplayAvatar/0/d41d8cd98f00b204e9800998ecf8427e/40" ]	[]	[]	[ "" ]	null	[]	null				Progress.com	https://www.progress.com/resources	Strong Revenue Growth, Global Market and Partner Expansion, and Customer Momentum Positions Company as the New Generation Enterprise Database Platform for Big Data. SAN CARLOS, CALIF. – April 14, 2015 – MarkLogic Corporation today announced record results for fiscal year 2015, which ended January 31. With revenue significantly north of $100 million, MarkLogic is proving to be a rapidly growing Enterprise NoSQL business that is succeeding in the enterprise where traditional databases are faltering. The company also increased annual bookings by more than 50 percent and marks one of the largest years of growth in MarkLogic’s history. Additionally, MarkLogic’s customer base grew by nearly 20 percent worldwide in industries such as financial services, healthcare, government, media and entertainment, and the public sector, while also expanding into new markets like agriculture, energy, insurance and transportation. MarkLogic’s business in Europe nearly doubled and bookings in Asia Pacific set a new company record. “This year marked a major shift in the industry: The Big Data market is beginning to segment. Our year-end results clearly demonstrate that MarkLogic has established itself as the de facto new generation, enterprise-hardened database platform for modern operational systems and applications,” said Gary Bloom, president and CEO of MarkLogic Corporation. “MarkLogic has consistently outperformed the competition by winning projects in the enterprise. In fact, over 50 percent of our business last year was derived from completing projects that started on Oracle but were better solved by MarkLogic. We will continue to focus on expanding our business globally into new markets, invest in talent and most importantly deliver on innovation that will help us solidify our market leadership. Our customers and partners are advanced in their thoughts and ideas on how to capitalize on all of their enterprise data and together we are evolving the operational database market as we know it today.” MarkLogic has emerged as the new generation database platform of choice by delivering a solution that helps enterprises not only analyze their data for better insight, but operationalize it to use in day-to-day activities that are crucial to business operations. Data has moved from powering products and decisions to competitive differentiation. With this new focus, corporate IT departments are now required to accommodate all of the disparate data residing in multiple systems that businesses and governments are managing today. Legacy relational database technology is often too limiting and inflexible to manage today’s ever-changing data types in the enterprise. Global organizations require a different approach to managing today’s data to understand context, analyze details, and most importantly, to put all of the data into action to accelerate business growth. MarkLogic is the only schema-agnostic Enterprise NoSQL database platform that integrates search, semantics, and application services, with enterprise features customers require for production applications. MarkLogic® software features ACID transactions for data reliability and transactional consistency, horizontal scaling, real-time indexing, high availability, disaster recovery, enterprise-grade security, tiered storage, and the most advanced support for the Hadoop Distributed File System (HDFS). New MarkLogic customers include: Aetna, Ascend Learning, Baltimore Museum of Art, Broadridge Financial, CABI, Cambridge University Press, Channel IQ, Department for Communities and Local Government (DCLG), Government Executive Media Group, Hannover Re, KSV 1870, Market 6, RCN Publishing Company, Répertoire International de Littérature Musicale (RILM), Spotta, and U.S. Navy. FY2015 highlights Leadership in the Operational Database Market In April, MarkLogic entered the Leaders Quadrant in the Gartner, Inc. Operational Database Management Systems Magic Quadrant. The report evaluated 25 different vendors and recognized MarkLogic as a Leader based on its completeness of vision and ability to execute. MarkLogic also ranked as a Leader in “The Forrester Wave™: NoSQL Document Databases, Q3 2014” by Forrester Research Inc. The company was positioned as a Leader based on its current offering, strategy and market presence. The report evaluated select companies against 57 criteria and stated that MarkLogic “has the most comprehensive NoSQL document databases, data management features and functionality to store, process, and access any kind of structured and multistructured data.” Thriving Partner Ecosystem MarkLogic greatly expanded its partner ecosystem on a global scale. Last year MarkLogic saw a 50 percent increase in the number of OEM customers using the MarkLogic Enterprise NoSQL database platform as the foundation for their solutions. The company secured OEM partnerships with companies like Capsicum, Hannover RE, KPMG LLP, TerraXML and Zavango. The company also further strengthened strategic partnerships with companies like Avalon Consulting LLP, Cognizant, EBCONT, EPAM Systems, Hexaware, ISDC, NTT DATA, Fujitsu Mission Critical Systems, Itochu-Techno-Solutions (CTC), and Smartlogic. In FY15, MarkLogic experienced more than a 100% increase in bookings from partners. “In the past few years, our business has thrived alongside our strategic partner MarkLogic. Both companies view today’s data outside of traditional rows and columns, understanding that data is everywhere and in every format,” said Tom Reidy, president & CEO, Avalon Consulting, LLC. “Together, we help our customers more easily and affordably integrate and transform all their data into actionable business results.” Broadening Board Expertise In November, MarkLogic appointed Greg J. Santora, former CFO of Intuit to the company’s board of directors. Greg is serving as Chairman of the Audit Committee to help scale the business to meet the rapid growth and the strong market demand for its new generation database platform. The company is establishing a board with members whose experience reflects the future path of the company. Expanding International Presence for Enterprise NoSQL MarkLogic continues its investment in non-U.S. markets, adding offices and making high-level strategic hires in Australia, France, Germany, Singapore, and Sweden. In addition, the company opened new offices in cities across the U.S. including Chicago and Houston. Setting the Standard for Next-Generation Databases with MarkLogic 8 Not resting on its laurels and enterprise advantage, MarkLogic continues to out-innovate competitors in order to deliver new features required by leading customers like Broadridge Financial. Specifically, MarkLogic recently introduced its next generation software MarkLogic version 8, the newest iteration of the company’s Enterprise NoSQL platform. This latest version supports server-side JavaScript and JSON and includes a robust set of enterprise features like semantics and bitemporal, which allows businesses to minimize risk by looking at data as it was over the course of time. These features are vital for companies in regulated industries like healthcare, financial services or utilities. In addition, MarkLogic is making it easy for organizations to deploy MarkLogic-powered applications in the cloud with a limited offering of a free, one-year version of its Enterprise NoSQL database platform on Amazon Web Services (AWS). “Our extensive testing has shown that MarkLogic 8 is going to shake up the market completely, and in particular, MarkLogic Bitemporal,” said Paolo Pelizzoli, Global Head of Architecture, Global Technology Operations, Broadridge Financial. “Broadridge plans to continue to use MarkLogic software as a strategic tool in helping customers perform exceptional analytics and increase performance in areas such as compliance management. This is due to MarkLogic’s ability to quickly and affordably let us build innovative apps with the additional bonuses of tiered storage, semantics and server-side JavaScript features and support.” Continuing with its enterprise leadership, MarkLogic Server 6.0-4 earned Common Criteria Certification through independent testing conducted by Leidos (formerly SAIC) and is the only Enterprise NoSQL database platform with NIAP Common Criteria Certification. To learn more about the MarkLogic Enterprise NoSQL database platform, attend MarkLogic World 2015, being held in various cities throughout the world. For more information or to register and meet with experts, attend sessions covering semantics, elasticity, tiered storage and more, please visit world.marklogic.com
correct_foundationPlace_00033	FactBench	2	40	https://www.kornferry.com/	en	Organizational Consulting	https://www.kornferry.co…ader_567x677.jpg	https://www.kornferry.co…ader_567x677.jpg	[ "https://www.kornferry.com/content/experience-fragments/kornferry-v2/en/header/master/_jcr_content/root/header/mainHeader%20containerMax%20d-flex%20justify-space-between%20align-center/headerLogo.coreimg.svg/1696363437761/kf-logo-green.svg", "https://www.kornferry.com/content/experience-fragments/kornferry-v2/en/header/master/_jcr_content/root/header/mainHeader%20containerMax%20d-flex%20justify-space-between%20align-center/homepageheaderLogo.coreimg.svg/1696363440634/kf-logo-white.svg", "https://www.kornferry.com/content/dam/kornferry-v2/floating-image/love-hope-leadership-side-img-932x635.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/floating-image/Workforce-2024-kf-homepage-exp.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/insights/briefings/issue-65/Issue65Assets_375x300.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/insights/twil/NoYoungHireJuly17.24MobileHome.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/insights/twil/PaySurveyJuly17.24MobileHome.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/home-page/clean-energy.jpg", "https://www.kornferry.com/content/dam/kornferry-v2/home-page/intelligence_cloud_laptop.png", "https://www.kornferry.com/content/dam/kornferry-v2/dev/KF%20Monogram.svg", "https://www.kornferry.com/content/experience-fragments/kornferry-v2/en/footer/master/_jcr_content/root/footer/footerLogo.coreimg.svg/1696363440634/kf-logo-white.svg" ]	[]	[]	[ "" ]	null	[]	null	Korn Ferry is a global organizational consulting firm. We work with our clients to design optimal organization structures, roles, and responsibilities. We help them hire the right people and advise them on how to reward and motivate their workforce while developing professionals as they navigate and advance their careers.	en	/etc.clientlibs/kornferry-v2/clientlibs/clientlib-site/resources/images/favicons/safari-pinned-tab.svg		https://www.kornferry.com/	We don't guess at success Using our talent platform, our business experts combine data from your organization and industry benchmarks with our insights as the leaders in creating organizational success, to show you exactly what you need to do to deliver results faster, consistently, and at scale. We know what success looks like. And how to get there.
correct_foundationPlace_00033	FactBench	1	42	https://stackoverflow.com/questions/44033482/marklogic-pass-empty-node-to-xquery-function	en	Marklogic pass empty node to xquery function	https://cdn.sstatic.net/…g?v=73d79a89bded	https://cdn.sstatic.net/…g?v=73d79a89bded	[ "https://cdn.sstatic.net/Img/teams/overflowai.svg?v=d706fa76cdae", "https://i.sstatic.net/Te6t0.jpg?s=64", "https://www.gravatar.com/avatar/9224acc8f0f1fab453f8f6ef36ffbad4?s=64&d=identicon&r=PG&f=y&so-version=2", "https://www.gravatar.com/avatar/1ae9b05545660e084ae3c3c65ef5a75a?s=64&d=identicon&r=PG&f=y&so-version=2", "https://stackoverflow.com/posts/44033482/ivc/b4ab?prg=3d26d3cd-b8cb-49af-91ac-97058684271b" ]	[]	[]	[ "" ]	null	[]	2017-05-17T19:40:35	This is a trivial simplification of my attempt to develop a function in the MarkLogic XQuery manager. The function I am trying to write must be capable of receiving a null node as input. I've been ...	en	https://cdn.sstatic.net/Sites/stackoverflow/Img/favicon.ico?v=ec617d715196	Stack Overflow	https://stackoverflow.com/questions/44033482/marklogic-pass-empty-node-to-xquery-function	Your problem is that your function expects exactly a single node() and not an empty-sequence()(which is what you're providing by calling your function like this: local:x( () )) An empty sequence can't be cast to a node. If you want to provide a function that expects zero or one nodes you can do it like this: declare function local:x($i as node()?) as xs:string* { let $x := "1" return $x (: Also instead of doing the above you could also simply return the string directly by simply typing it out: "1" :) }; The question mark is the key here: Some functions accept a single value or the empty sequence as an argument and some may return a single value or the empty sequence. This is indicated in the function signature by following the parameter or return type name with a question mark: "?", indicating that either a single value or the empty sequence must appear. (Taken from W3C) One thing you should be aware about is that an empty sequence is not the same as e.g. an empty text node! let $emptySeq := () (:This actually has no value at all:) let $emptyText := text {} (:This simply is an empty node, but it is still a node!:) return (fn:empty($emptySeq), fn:empty($emptyText))
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"https://db-engines.com/up.gif", "https://db-engines.com/info.png", "https://db-engines.com/up.gif", "https://db-engines.com/down.gif", "https://db-engines.com/pictures/Email.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/Facebook.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/Mastodon.svg", "https://db-engines.com/pictures/Bluesky.png" ]	[]	[]	[ "" ]	null	[]	null	The DB-Engines Ranking shows the popularity of 421 database management systems	en		DB-Engines	https://db-engines.com/en/ranking	Select a ranking Complete ranking Relational DBMS Key-value stores Document stores Time Series DBMS Graph DBMS Search engines Object oriented DBMS RDF stores Vector DBMS Wide column stores Multivalue DBMS Spatial DBMS Native XML DBMS Event Stores Content stores Navigational DBMS Special reports Ranking by database model Open source vs. 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Read more about the method of calculating the scores. 421 systems in ranking, July 2024RankDBMSDatabase ModelScoreJul 2024Jun 2024Jul 2023Jul 2024Jun 2024Jul 20231.1.1.Oracle Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Graph DBMS RDF store Spatial DBMS Vector DBMS1240.37-3.72-15.642.2.2.MySQL Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Spatial DBMS1039.46-21.89-110.893.3.3.Microsoft SQL Server Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS807.65-13.91-113.954.4.4.PostgreSQL Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS Vector DBMS638.91+2.66+21.085.5.5.MongoDB Detailed vendor-provided information availableDocument, Multi-model Document store Spatial DBMS Search engine Time Series DBMS Vector DBMS429.83+8.75-5.676.6.6.Redis Detailed vendor-provided information availableKey-value, Multi-model Key-value store Document store Graph DBMS Spatial DBMS Search engine Time Series DBMS Vector DBMS156.77+0.82-7.007. 8. 11.Snowflake Detailed vendor-provided information availableRelational136.53+6.17+18.848. 7.8.ElasticsearchSearch engine, Multi-model Search engine Document store Spatial DBMS Vector DBMS130.82-2.01-8.779.9. 7.IBM Db2Relational, Multi-model Relational DBMS Document store RDF store Spatial DBMS124.40-1.50-15.4110.10.10.SQLite Detailed vendor-provided information availableRelational109.95-1.46-20.2511.11. 9.Microsoft AccessRelational100.63-0.53-30.0912.12.12.Apache Cassandra Detailed vendor-provided information availableWide column, Multi-model Wide column store Vector DBMS99.13+0.30-7.4013. 14. 14.SplunkSearch engine92.92+3.82+5.8014. 13. 13.MariaDB Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS90.58-0.45-5.5215.15. 18.Databricks Detailed vendor-provided information availableMulti-model Document store Relational DBMS83.29+2.21+14.8316.16. 15.Microsoft Azure SQL DatabaseRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS76.75-0.03-2.2117.17. 16.Amazon DynamoDB Detailed vendor-provided information availableMulti-model Document store Key-value store70.95-3.50-7.8618. 19. 20.Google BigQuery Detailed vendor-provided information availableRelational57.82-0.28+2.4019. 18. 17.Apache HiveRelational57.29-2.46-15.5820.20. 21.FileMakerRelational48.59+0.68-4.7321.21. 22.Neo4j Detailed vendor-provided information availableGraph45.75+0.86-6.3122.22. 19.TeradataRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS Time Series DBMS44.52-0.35-15.7323.23.23.SAP HANA Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Graph DBMS Spatial DBMS44.12-0.15-6.6024.24.24.Apache SolrSearch engine, Multi-model Search engine Spatial DBMS38.88-2.15-9.6825.25.25.SAP Adaptive ServerRelational, Multi-model Relational DBMS Spatial DBMS34.74-0.34-8.1326.26. 27.Apache HBaseWide column27.73-0.23-8.3727.27. 26.Microsoft Azure Cosmos DB Detailed vendor-provided information availableMulti-model Document store Graph DBMS Key-value store Wide column store Spatial DBMS27.12-0.59-9.3828.28.28.InfluxDB Detailed vendor-provided information availableTime Series, Multi-model Time Series DBMS Spatial DBMS23.59-0.79-7.3329.29.29.PostGISSpatial, Multi-model Spatial DBMS Relational DBMS20.96-0.76-9.0530.30. 31.FirebirdRelational20.41-0.10-5.7531.31. 30.Microsoft Azure Synapse AnalyticsRelational19.84-0.09-7.7832.32. 33.MemcachedKey-value17.74-0.34-4.3433.33. 37.Apache Spark (SQL)Relational17.70-0.34-1.9334.34.34.InformixRelational, Multi-model Relational DBMS Document store Spatial DBMS Time Series DBMS17.56+0.45-4.3635. 37. 46.OpenSearch Detailed vendor-provided information availableSearch engine, Multi-model Search engine Vector DBMS16.64+0.61+3.7736.36. 32.Couchbase Detailed vendor-provided information availableDocument, Multi-model Document store Key-value store Spatial DBMS Search engine Time Series DBMS Vector DBMS16.41-0.17-8.7237. 38. 40.ClickHouse Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Time Series DBMS15.17-0.38-0.1338. 35. 35.Amazon Redshift Detailed vendor-provided information availableRelational15.07-1.82-5.2939.39. 38.Firebase Realtime DatabaseDocument13.87+0.23-4.8540.40. 36.Apache ImpalaRelational, Multi-model Relational DBMS Document store12.57+0.13-7.0941.41. 45.Apache FlinkRelational10.23-1.07-3.5642.42. 41.VerticaRelational, Multi-model Relational DBMS Spatial DBMS Time Series DBMS9.96-0.10-5.2043. 44.43.dBASERelational9.52-0.18-5.1744. 43. 42.PrestoRelational8.49-1.26-6.3845. 48. 48.GreenplumRelational, Multi-model Relational DBMS Document store Spatial DBMS8.23+0.16-2.1046. 45. 39.NetezzaRelational8.15-0.43-7.4747. 46. 52.H2Relational, Multi-model Relational DBMS Spatial DBMS8.01-0.32-0.8548. 47. 44.CouchDBDocument, Multi-model Document store Spatial DBMS7.82-0.48-6.5849.49. 55.Kdb Detailed vendor-provided information availableMulti-model Time Series DBMS Vector DBMS Relational DBMS7.58-0.12-0.6450. 52. 54.RealmDocument7.34-0.07-0.9151. 50. 56.PrometheusTime Series7.33-0.35-0.7252. 51. 50.Amazon AuroraRelational, Multi-model Relational DBMS Document store7.32-0.25-2.0753. 54. 51.etcdKey-value6.99-0.04-2.0154. 53. 47.Google Cloud FirestoreDocument6.75-0.61-3.6155. 57. 49.Oracle EssbaseRelational5.98+0.05-3.5756. 55. 70.SphinxSearch engine5.96+0.01-0.1257. 59. 68.Microsoft Azure AI SearchSearch engine, Multi-model Search engine Vector DBMS5.68+0.15-0.6758. 64. 57.AlgoliaSearch engine5.57+0.41-2.3659. 65. 63.Trino Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Key-value store Spatial DBMS Search engine Time Series DBMS Wide column store5.44+0.45-1.1960. 61. 58.HazelcastKey-value, Multi-model Key-value store Document store5.32-0.14-2.4761. 60. 66.Aerospike Detailed vendor-provided information availableMulti-model Document store Graph DBMS Key-value store Spatial DBMS Vector DBMS5.16-0.35-1.3262. 66. 65.Apache JackrabbitContent5.15+0.25-1.3863. 56. 59.Datastax Enterprise Detailed vendor-provided information availableWide column, Multi-model Wide column store Document store Graph DBMS Spatial DBMS Search engine Vector DBMS5.10-0.83-2.1064. 67. 74.GraphiteTime Series5.04+0.22-0.7865. 69. 106.DuckDBRelational4.79+0.16+1.4366. 58. 60.CockroachDBRelational4.45-1.28-2.5967. 63. 53.MarkLogicMulti-model Document store Native XML DBMS RDF store Search engine4.42-0.75-4.1768. 70. 62.Apache DerbyRelational4.42-0.18-2.3969. 68. 64.EhcacheKey-value4.26-0.39-2.3470. 62. 69.SingleStore Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Spatial DBMS Time Series DBMS Vector DBMS4.12-1.26-2.0871. 72. 75.Google Cloud DatastoreDocument4.06-0.30-1.5672. 73. 83.Virtuoso Detailed vendor-provided information availableMulti-model Document store Graph DBMS Native XML DBMS Relational DBMS RDF store Search engine Spatial DBMS3.98-0.29-0.9173. 79. 76.Riak KVKey-value3.95-0.07-1.5874. 71. 84.TimescaleDBTime Series, Multi-model Time Series DBMS Relational DBMS3.90-0.55-0.8875. 76. 67.ScyllaDB Detailed vendor-provided information availableWide column, Multi-model Wide column store Key-value store3.84-0.24-2.5376. 75. 73.InterbaseRelational3.83-0.25-2.1777. 82. 72.IngresRelational3.800.00-2.2078.78. 97.DolphinDBTime Series, Multi-model Time Series DBMS Relational DBMS3.62-0.40-0.2379. 80. 77.SAP SQL AnywhereRelational3.59-0.36-1.9180. 85. 90.OpenEdgeRelational3.58+0.14-0.7281.81. 61.Microsoft Azure Data ExplorerRelational, Multi-model Relational DBMS Document store Event Store Spatial DBMS Search engine Time Series DBMS3.57-0.23-3.3582. 74. 107.TiDB Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store3.46-0.79+0.1783. 87. 82.Apache AccumuloWide column3.46+0.12-1.4884. 77. 71.Microsoft Azure Table StorageWide column3.42-0.63-2.6185. 88. 87.ArangoDB Detailed vendor-provided information availableMulti-model Document store Graph DBMS Key-value store Search engine3.42+0.15-1.1586. 93. 80.HyperSQLRelational3.28+0.05-1.7987. 84. 105.InterSystems IRIS Detailed vendor-provided information availableMulti-model Document store Key-value store Object oriented DBMS Relational DBMS3.24-0.29-0.1588. 92. 141.PineconeVector3.13-0.10+0.8789. 103. 193.Milvus Detailed vendor-provided information availableVector3.12+0.34+1.7690. 89. 85.OrientDBMulti-model Document store Graph DBMS Key-value store3.07-0.18-1.6991. 83. 78.Apache Jena - TDBRDF3.05-0.57-2.1692. 96. 81.Apache IgniteMulti-model Key-value store Relational DBMS3.02-0.09-1.9793. 90. 108.Apache DruidMulti-model Relational DBMS Time Series DBMS2.94-0.31-0.2994. 97. 96.Oracle NoSQLMulti-model Document store Key-value store Relational DBMS2.93-0.12-0.9295. 94. 124.Memgraph Detailed vendor-provided information availableGraph2.93-0.26+0.2396. 95. 86.Google Cloud BigtableMulti-model Key-value store Wide column store2.93-0.23-1.7697. 86. 92.RocksDB Detailed vendor-provided information availableKey-value2.89-0.52-1.3498. 91. 133.GraphDB Detailed vendor-provided information availableMulti-model Graph DBMS RDF store2.87-0.38+0.3599. 101. 91.RavenDBDocument, Multi-model Document store Graph DBMS Spatial DBMS Time Series DBMS2.80-0.03-1.48100.100. 93.Google Cloud SpannerRelational2.77-0.07-1.30101. 99. 94.GemFireKey-value, Multi-model Key-value store Document store2.74-0.10-1.30102.102. 79.AdabasMultivalue2.73-0.07-2.42103. 104. 95.IBM CloudantDocument2.67-0.08-1.22104. 105. 134.QuestDB Detailed vendor-provided information availableTime Series, Multi-model Time Series DBMS Relational DBMS2.66-0.05+0.23105. 108. 88.SAP IQRelational2.62-0.02-1.94106. 107. 100.RethinkDBDocument, Multi-model Document store Spatial DBMS2.53-0.13-1.11107. 106. 118.TDengine Detailed vendor-provided information availableTime Series, Multi-model Time Series DBMS Relational DBMS2.45-0.23-0.48108. 111. 98.InfinispanKey-value2.40-0.05-1.39109. 98. 89.UniData,UniVerseMultivalue2.37-0.48-2.09110. 109. 99.YugabyteDB Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Wide column store2.32-0.31-1.41111. 112. 110.PouchDBDocument2.31-0.03-0.83112. 110. 115.4DRelational2.31-0.16-0.77113. 114. 102.MaxDBRelational2.20-0.06-1.42114. 115. 103.LevelDBKey-value2.19-0.06-1.24115. 113. 120.Amazon NeptuneMulti-model Graph DBMS RDF store2.14-0.15-0.76116. 117. 131.CitusRelational, Multi-model Relational DBMS Document store2.09-0.05-0.48117. 122. 151.StardogMulti-model Graph DBMS RDF store2.070.00-0.01118. 119. 111.Percona Server for MySQLRelational2.04-0.06-1.07119. 129. 122.Oracle CoherenceKey-value2.02+0.05-0.76120. 116. 130.NebulaGraph Detailed vendor-provided information availableGraph2.01-0.22-0.59121. 120. 152.GridDB Detailed vendor-provided information availableTime Series, Multi-model Time Series DBMS Key-value store Relational DBMS1.99-0.11-0.08122. 118. 128.CoveoSearch engine1.98-0.13-0.66123. 121. 116.LMDBKey-value1.97-0.11-0.99124.124. 117.Apache DrillMulti-model Document store Relational DBMS1.95-0.07-0.98125. 128. 119.CloudKitDocument1.92-0.07-1.00126. 123. 132.Apache PhoenixRelational1.91-0.15-0.64127. 126. 101.Oracle Berkeley DBMulti-model Key-value store Native XML DBMS1.90-0.10-1.73128. 125. 125.JanusGraphGraph1.89-0.13-0.78129. 127. 135.ChromaVector1.88-0.12-0.53130.130. 114.EDB PostgresRelational, Multi-model Relational DBMS Document store Spatial DBMS1.88-0.03-1.21131.131. 154.Amazon DocumentDBDocument1.87-0.03-0.14132. 133. 129.Amazon SimpleDBKey-value1.86-0.02-0.75133. 137. 144.Amazon CloudSearchSearch engine1.79-0.02-0.46134. 132. 104.RRDtoolTime Series1.75-0.15-1.68135. 136. 139.EmpressRelational1.74-0.09-0.55136. 139. 126.EXASOLRelational1.71-0.05-0.94137. 141. 145.MonetDBRelational, Multi-model Relational DBMS Document store Spatial DBMS1.70-0.03-0.51138. 149. 121.OceanBaseRelational, Multi-model Relational DBMS Document store Wide column store1.69+0.11-1.17139. 135. 138.BaseXNative XML1.69-0.15-0.61140. 147. 127.IMSNavigational1.65+0.03-1.00141. 142. 140.OpenTSDBTime Series1.62-0.06-0.66142. 134. 123.GeodeKey-value1.62-0.24-1.16143.143. 157.TarantoolMulti-model Document store Key-value store Relational DBMS Spatial DBMS1.59-0.08-0.35144. 146. 136.SpatiaLiteSpatial, Multi-model Spatial DBMS Relational DBMS1.59-0.04-0.77145. 138. 147.TigerGraphGraph1.59-0.22-0.59146. 144. 143.DatomicRelational1.57-0.08-0.69147. 145. 109.HEAVY.AIRelational, Multi-model Relational DBMS Spatial DBMS1.52-0.12-1.65148. 153. 201.Weaviate Detailed vendor-provided information availableVector1.51-0.01+0.24149. 154. 158.Actian NoSQL DatabaseObject oriented1.50-0.01-0.38150. 151. 167.FaunaMulti-model Document store Graph DBMS Relational DBMS Time Series DBMS1.48-0.07-0.19151. 157. 149.VoltDBRelational1.45-0.02-0.68152. 150. 146.GridGainMulti-model Key-value store Relational DBMS1.44-0.11-0.76153. 152. 170.DgraphGraph1.42-0.11-0.23154. 148. 137.TiberoRelational1.42-0.17-0.90155. 156. 142.jBASEMultivalue1.39-0.10-0.87156. 158. 148.Db4oObject oriented1.39-0.02-0.76157. 162. 153.ObjectStoreObject oriented1.38+0.04-0.68158. 140. 150.FireboltRelational1.35-0.38-0.73159. 160. 156.IBM Db2 warehouseRelational1.31-0.06-0.66160. 169. 166.mSQLRelational1.29+0.02-0.39161. 165. 162.MnesiaDocument1.28-0.01-0.54162. 155. 178.PlanetScaleRelational, Multi-model Relational DBMS Document store Spatial DBMS1.27-0.22-0.27163. 167. 275.QdrantVector1.26-0.02+0.65164. 161. 159.TimesTenRelational1.26-0.10-0.61165. 163. 203.D3Multivalue1.25-0.08-0.01166. 174. 194.HFSQLRelational1.24+0.06-0.11167. 164. 242.Apache IoTDBTime Series1.24-0.08+0.41168.168. 161.LiteDBDocument1.21-0.07-0.64169. 175. 173.GiraphGraph1.16-0.01-0.43170. 176. 163.DatameerDocument1.15-0.02-0.63171. 170. 185.Apache KylinRelational1.14-0.11-0.31172.172. 200.VictoriaMetricsTime Series1.12-0.11-0.17173. 166. 171.ObjectBoxMulti-model Object oriented DBMS Vector DBMS Time Series DBMS1.12-0.16-0.50174. 177. 209.Amazon TimestreamTime Series1.12-0.04-0.07175. 171. 155.SQLBaseRelational1.11-0.13-0.87176. 183. 177.DataEaseRelational1.070.00-0.47177. 159. 165.SednaNative XML1.06-0.33-0.64178. 180. 181.Apache HAWQRelational1.05-0.07-0.45179. 184. 187.openGaussRelational, Multi-model Relational DBMS Document store Spatial DBMS1.04-0.02-0.37180. 190. 243.SurrealDBMulti-model Document store Graph DBMS1.04+0.02+0.21181. 173. 174.EventStoreDBEvent1.04-0.15-0.51182. 178. 176.Oracle RdbRelational1.04-0.10-0.50183. 182. 168.NonStop SQLRelational1.04-0.04-0.64184. 186. 183.GBaseRelational1.03-0.02-0.46185.185. 184.FoundationDBMulti-model Document store Key-value store Relational DBMS1.02-0.05-0.46186. 181. 164.GT.MKey-value1.00-0.09-0.72187. 193. 199.MeilisearchSearch engine0.97-0.03-0.33188. 191. 189.DoltRelational, Multi-model Relational DBMS Document store0.95-0.07-0.44189. 199. 217.M3DBTime Series0.95+0.03-0.14190. 187. 169.CubridRelational0.95-0.09-0.72191. 188. 175.GigaSpacesMulti-model Document store Object oriented DBMS Graph DBMS Search engine0.94-0.09-0.61192. 202. 218.Amazon KeyspacesWide column0.92+0.01-0.11193. 195. 190.NCache Detailed vendor-provided information availableKey-value, Multi-model Key-value store Document store Search engine0.91-0.05-0.47194. 200. 207.IDMSNavigational0.910.00-0.31195. 192. 172.InfobrightRelational0.91-0.11-0.70196. 189. 179.AltibaseRelational0.91-0.12-0.63197. 212. 233.RocksetDocument, Multi-model Document store Relational DBMS Search engine0.89+0.07+0.00198. 179. 202.AllegroGraphMulti-model Document store Graph DBMS RDF store Vector DBMS Spatial DBMS0.89-0.24-0.38199. 201. 260.Alibaba Cloud AnalyticDB for MySQL Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store0.88-0.03+0.16200. 204. 186.MatrixOneRelational0.870.00-0.56201. 197. 197.NuoDBRelational0.87-0.07-0.47202. 206. 195.HPE Ezmeral Data FabricMulti-model Document store Wide column store0.86+0.01-0.49203. 210. 196.ZODBKey-value0.84+0.00-0.50204. 207. 213.TDSQL for MySQL Detailed vendor-provided information availableRelational, Multi-model Relational DBMS Document store Spatial DBMS0.84-0.01-0.29205. 203. 208.VitessRelational, Multi-model Relational DBMS Document store Spatial DBMS0.83-0.04-0.38206. 211. 192.Model 204Multivalue0.83-0.01-0.55207. 196. 227.YellowbrickRelational0.82-0.14-0.15208. 205. 221.GeoMesaSpatial0.80-0.06-0.22209. 208. 225.BigchainDBDocument0.79-0.05-0.19210. 194. 188.Northgate RealityMultivalue0.79-0.19-0.60211. 209. 251.Alibaba Cloud MaxComputeRelational0.78-0.07-0.01212. 217. 232.Datacom/DBRelational0.78+0.01-0.13213. 221. 223.SciDBMultivalue0.77+0.02-0.23214. 218. 191.Actian VectorRelational0.77+0.01-0.61215. 235. 298.Alibaba Cloud PolarDB Detailed vendor-provided information availableRelational0.76+0.10+0.29216. 215. 229.BoltDBKey-value0.76-0.04-0.18217. 223. 198.XapianSearch engine0.75+0.01-0.56218. 198. 219.StarRocksRelational0.75-0.18-0.28219. 216. 216.eXist-dbNative XML0.75-0.03-0.35220.220. 287.Alibaba Cloud AnalyticDB for PostgreSQLRelational0.73-0.02+0.19221. 213. 222.BlazegraphMulti-model Graph DBMS RDF store0.73-0.07-0.27222. 227. 231.CrateDBMulti-model Document store Spatial DBMS Search engine Time Series DBMS Vector DBMS Relational DBMS0.72+0.01-0.19223. 231. 210.WebSphere eXtreme ScaleKey-value0.70+0.03-0.46224. 219. 234.TypesenseSearch engine0.70-0.06-0.17225.225. 253.Objectivity/DBObject oriented0.68-0.04-0.08226.226. 204.1010dataRelational0.67-0.04-0.57227. 228. 205.solidDBRelational0.67-0.04-0.56228. 222. 215.RDF4JRDF0.65-0.09-0.45229. 236. 214.DBISAMRelational0.65+0.00-0.47230. 224. 245.SQream DBRelational0.64-0.10-0.17231. 242. 285.VespaMulti-model Search engine Vector DBMS0.64+0.02+0.09232.232. 258.Graph EngineMulti-model Graph DBMS Key-value store0.64-0.04-0.11233. 214. 249.eXtremeDBMulti-model Relational DBMS Time Series DBMS0.64-0.17-0.15234. 240. 239.FrontBaseRelational0.64+0.01-0.21235. 239. 230.ObjectDBObject oriented0.640.00-0.28236. 230. 212.TypeDB Detailed vendor-provided information availableMulti-model Graph DBMS Object oriented DBMS Relational DBMS0.63-0.07-0.52237. 229. 261.KeyDBKey-value0.63-0.08-0.09238. 233. 241.KairosDBTime Series0.60-0.07-0.24239. 294. 267.4storeRDF0.58+0.27-0.11240. 238.RisingWaveRelational0.57-0.07241. 250. 228.NexusDBRelational0.57+0.02-0.39242. 244. 274.HarperDBDocument0.56-0.05-0.06243. 249. 247.GemStone/SObject oriented0.550.00-0.26244. 241. 180.SQL.JSRelational0.54-0.09-0.97245. 253. 252.R:BASERelational0.54+0.00-0.23246. 252. 244.Splice MachineRelational0.540.00-0.28247. 245. 206.HibariKey-value0.53-0.07-0.69248. 237. 182.PerstObject oriented0.53-0.12-0.96249. 255. 220.MapDBKey-value0.53+0.01-0.50250. 251. 262.ScalarisKey-value0.53-0.01-0.19251. 246. 257.Percona Server for MongoDBDocument0.52-0.08-0.23252. 264. 224.ScaleArcRelational0.51+0.05-0.48253. 234. 235.KineticaRelational, Multi-model Relational DBMS Spatial DBMS Time Series DBMS0.51-0.15-0.36254. 243. 240.atotiObject oriented0.50-0.11-0.34255. 247. 272.Apache DorisRelational0.50-0.10-0.15256. 271. 279.TajoRelational0.47+0.06-0.10257. 262. 256.VistaDBRelational0.470.00-0.28258. 254. 266.Postgres-XLRelational, Multi-model Relational DBMS Document store Spatial DBMS0.46-0.07-0.23259. 256. 263.AlaSQLMulti-model Document store Relational DBMS0.45-0.06-0.26260. 268. 246.RasdamanMultivalue0.430.00-0.38261. 269. 236.OpenInsightMultivalue0.42-0.01-0.45262. 266. 289.Kyligence EnterpriseRelational0.42-0.04-0.12263. 257. 259.KingbaseRelational, Multi-model Relational DBMS Document store Spatial DBMS0.42-0.09-0.31264. 259. 270.Raima Database Manager Detailed vendor-provided information availableMulti-model Relational DBMS Time Series DBMS0.42-0.08-0.24265. 260. 264.LokiJSDocument0.41-0.08-0.30266. 261. 378.Dragonfly Detailed vendor-provided information availableKey-value0.41-0.08+0.28267. 270. 310.CnosDBTime Series0.41-0.01+0.03268. 272. 271.StrabonRDF0.41+0.01-0.25269. 258. 276.SequoiadbMulti-model Document store Relational DBMS0.40-0.10-0.21270. 248. 237.ValdVector0.40-0.20-0.46271. 277. 250.ModeShapeContent0.39+0.02-0.40272. 263. 303.Alibaba Cloud Log Service Detailed vendor-provided information availableSearch engine0.37-0.10-0.05273. 275. 248.Apache PinotRelational0.37-0.01-0.43274. 285. 255.SearchBloxSearch engine0.37+0.03-0.39275. 281. 295.Project VoldemortKey-value0.36+0.01-0.12276. 273. 286.InfiniteGraphGraph0.36-0.03-0.19277. 293. 268.RedlandRDF0.36+0.04-0.33278. 288.Apache SedonaSpatial0.34+0.01279. 267. 265.ITTIATime Series, Multi-model Time Series DBMS Relational DBMS0.34-0.10-0.36280. 265. 238.HeroicTime Series0.33-0.13-0.52281. 305. 254.StarcounterObject oriented0.33+0.05-0.43282. 313. 306.PipelineDBRelational0.32+0.08-0.07283. 290. 300.Comdb2Relational0.32+0.00-0.13284. 287. 281.YDBMulti-model Document store Relational DBMS0.32-0.01-0.24285. 291. 283.FlureeGraph0.320.00-0.24286. 274. 308.Cloudflare Workers KVKey-value0.30-0.08-0.09287. 279. 320.MarqoSearch engine0.30-0.06-0.05288. 298. 284.Deep LakeVector0.30-0.01-0.25289. 299. 291.ElassandraWide column, Multi-model Wide column store Search engine0.29-0.01-0.21290. 280. 316.LeanXcaleMulti-model Key-value store Relational DBMS0.28-0.08-0.08291. 308. 293.Mimer SQLRelational0.28+0.01-0.22292. 278. 345.Fujitsu Enterprise PostgresRelational, Multi-model Relational DBMS Document store Spatial DBMS0.28-0.09+0.04293. 282. 292.AxibaseTime Series0.27-0.07-0.22294. 283. 280.DatabendRelational0.27-0.07-0.30295. 311. 269.JadeObject oriented0.26+0.00-0.40296. 284. 273.OpenQMMultivalue0.26-0.08-0.36297. 276. 304.BrytlytRelational0.26-0.11-0.15298. 286. 278.LovefieldRelational0.25-0.09-0.35299. 295. 336.ImmudbKey-value, Multi-model Key-value store Relational DBMS0.24-0.07-0.04300. 323. 288.Actian FastObjectsObject oriented0.23+0.03-0.31301. 318. 299.FlockDBGraph0.23+0.01-0.22302. 314. 353.Kyoto TycoonKey-value0.23-0.02+0.00303. 302. 359.Manticore SearchSearch engine, Multi-model Search engine Time Series DBMS0.23-0.06+0.03304. 321. 334.Tibco ComputeDBRelational0.23+0.02-0.05305. 303. 324.AnzoGraph DBMulti-model Graph DBMS RDF store0.22-0.07-0.10306. 289. 358.PieCloudDB Detailed vendor-provided information availableRelational0.22-0.10+0.02307. 319.307.ElevateDBRelational0.21-0.01-0.18308. 306.YottaDBKey-value, Multi-model Key-value store Relational DBMS0.21-0.07309. 301. 351.Alibaba Cloud TSDBTime Series0.21-0.08-0.02310. 292. 317.FeatureBaseRelational0.21-0.11-0.15311. 297. 330.Alibaba Cloud Table StoreWide column0.20-0.11-0.10312. 310. 311.Speedb Detailed vendor-provided information availableKey-value0.20-0.07-0.18313. 327. 309.RedStoreRDF0.200.00-0.18314. 300. 325.RDFoxMulti-model Graph DBMS RDF store Relational DBMS0.19-0.10-0.13315. 328. 361.MyScaleMulti-model Relational DBMS Vector DBMS0.19-0.01+0.00316. 304. 344.Faircom DBMulti-model Key-value store Relational DBMS0.19-0.10-0.06317. 315. 392.AgensGraphMulti-model Graph DBMS Relational DBMS0.19-0.05+0.12318. 296. 294.EJDBDocument0.19-0.12-0.30319. 326. 282.HyperGraphDBGraph0.18-0.02-0.38320. 316. 323.TerminusDBGraph, Multi-model Graph DBMS Document store RDF store0.18-0.06-0.16321. 333. 313.TransLatticeRelational0.170.00-0.20322. 325. 356.Valentina ServerRelational0.17-0.04-0.04323. 336. 315.NEventStoreEvent0.16-0.01-0.20324. 307. 342.Riak TSTime Series0.16-0.12-0.11325. 331. 301.Tokyo TyrantKey-value0.16-0.02-0.27326. 309. 366.IBM Db2 Event StoreMulti-model Event Store Time Series DBMS0.15-0.11-0.02327. 340.327.GraphBaseGraph0.15+0.00-0.16328. 312. 354.EsgynDBRelational0.14-0.10-0.07329. 343. 319.XtremeDataRelational0.14+0.00-0.21330. 335. 322.Apache 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396.SmallSQLRelational0.02+0.00+0.00367. 396. 398.SparkleDBRDF0.02+0.00+0.00368. 355. 385.OushuDBRelational0.01-0.09-0.09369. 397. 370.AcebaseDocument0.01+0.00-0.12370. 398. 399.Resin CacheKey-value0.010.000.00371. 366. 357.Hawkular MetricsTime Series0.01-0.07-0.19372. 364. 401.SWC-DBWide column, Multi-model Wide column store Time Series DBMS0.01-0.07+0.00373. 362. 395.Faircom EDGEMulti-model Key-value store Relational DBMS0.01-0.08-0.01374. 399. 341.EloqueraObject oriented0.010.00-0.26375. 400. 346.SiaqodbObject oriented0.00+0.00-0.24376. 401. 369.LedisDBKey-value0.00+0.00-0.14377. 387. 404.SwayDBKey-value0.00-0.04+0.00378. 402. 387.ActorDBRelational0.00±0.00-0.09378. 358. 350.ArcadeDBMulti-model Document store Graph DBMS Key-value store Time Series DBMS0.00-0.10-0.23378. 402. 405.BergDBKey-value0.00±0.00±0.00378. 402. 329.BrightstarDBRDF0.00±0.00-0.30378. 388. 390.Cachelot.ioKey-value0.00-0.04-0.07378. 376.chDBRelational, Multi-model Relational DBMS Time Series DBMS0.00-0.07378. 402. 405.CortexDBMulti-model Document store Key-value store0.00±0.00±0.00378. 402. 405.CovenantSQLRelational0.00±0.00±0.00378. 402. 405.DaggerDBRelational0.00±0.00±0.00378. 402. 376.Edge IntelligenceRelational0.00±0.00-0.13378. 402. 405.EdgelessDBRelational0.00±0.00±0.00378. 377. 388.GalaxybaseGraph0.00-0.07-0.08378. 368. 340.H2GISSpatial, Multi-model Spatial DBMS Relational DBMS0.00-0.08-0.27378. 402. 405.HeliumKey-value0.00±0.00±0.00378. 402. 405.HGraphDBGraph0.00±0.00±0.00378. 402. 360.HyperLevelDBKey-value0.00±0.00-0.19378. 402. 373.iBoxDBDocument0.00±0.00-0.14378. 395. 380.IndicaSearch engine0.00-0.02-0.12378. 365. 377.InfinityDBKey-value0.00-0.08-0.13378. 381. 402.JaguarDBMulti-model Key-value store Vector DBMS0.00-0.06-0.01378. 402. 405.JasDBDocument0.00±0.00±0.00378. 402. 405.K-DBRelational0.00±0.00±0.00378. 402.KuzuGraph0.00±0.00378. 375. 403.NewtsTime Series0.00-0.070.00378. 369. 364.NSDbTime Series0.00-0.08-0.18378. 370.OpenTenBaseRelational0.00-0.08378. 380. 373.OrigoDBMulti-model Document store Object oriented DBMS0.00-0.06-0.14378. 402. 368.RaptorDBDocument0.00±0.00-0.15378. 384.ReductStoreTime Series0.00-0.05378. 402. 405.RizhiyiSearch engine, Multi-model Search engine Time Series DBMS0.00±0.00±0.00378. 373. 405.Sadas EngineRelational0.00-0.07±0.00378. 390. 405.searchxmlMulti-model Native XML DBMS Search engine0.00-0.03±0.00378.378. 367.SiriDBTime Series0.00-0.07-0.17378. 392. 405.SpaceTimeSpatial, Multi-model Spatial DBMS Relational DBMS0.00-0.03±0.00378. 367. 371.TerarkDBKey-value0.00-0.08-0.14378. 372. 405.TkrzwKey-value0.00-0.07±0.00378. 402. 343.TomP2PKey-value0.00±0.00-0.25378. 348. 394.Transwarp ArgoDBRelational, Multi-model Relational DBMS Search engine0.00-0.12-0.03378. 386.Transwarp HippoVector0.00-0.05378. 341. 386.Transwarp KunDBRelational0.00-0.14-0.10378. 371. 405.Transwarp StellarDBGraph0.00-0.07±0.00378. 402. 333.UpscaledbKey-value0.00±0.00-0.29378. 354. 382.VelocityDBMulti-model Graph DBMS Object oriented 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correct_foundationPlace_00033	FactBench	1	39	https://cleverllamas.com/articles/reviews/optic/geospatial/	en	Optic Geospatial			[ "https://cleverllamas.com/images/llama.svg", "https://cleverllamas.com/assets/llamaverse-8d83fb32.png", "https://cleverllamas.com/assets/multiple-documents-8ed317d8.png", "https://cleverllamas.com/assets/projected-pastures-d992eeb3.png", "https://cleverllamas.com/assets/who-is-where-40bec2ae.png", "https://cleverllamas.com/assets/who-is-where-full-2e94b476.png", "https://cleverllamas.com/assets/who-is-naughty-table-9a282874.png", "https://cleverllamas.com/assets/who-is-naughty-74718ade.png", "https://cleverllamas.com/assets/who-mixes-ec415488.png", "https://cleverllamas.com/assets/who-moved-6268f066.png", "https://cleverllamas.com/assets/which-way-did-they-go-471496eb.png", "https://cleverllamas.com/images/llamas.svg" ]	[]	[]	[ "" ]	null	[]	null	We Master MarkLogic	en			null	Geospatial features in MarkLogic are not new. They have been around since the start of MarkLogic 9.0. However, they have been limited to the MarkLogic CTS related APIs. With the release of MarkLogic 11, the Optic API now supports geospatial features. This article will explore the use of the Optic API for geospatial features. # What did MarkLogic have for Geospatial features prior to MarkLogic 11? The geospatial implementation started like some other new features - a new index type and appropriate tooling to support it. The geospatial index supports many types of spatial features such as complex polygons, points, lines, and circles. In MarkLogic prior to 11, like all indexes, they are scoped to a fragment. For most use-cases, this translates to a document. You could easily add many geospatial features into a single document. These will index fine and are searchable using various geospatial related search functions. However, to find out "which" feature in your document matched requires post-processing (filtering) in some manner or other. This also leads to double-querying (post-process to then re-match the correct version). This really limited the use-cases in which one might want or need more than one geospatial feature in a single document. In Summary - Document Oriented: Multiple geospatial artifacts allowed and indexed But âWhich one matchedâ takes work: Searchable expression(filtered) - or walk the document yourself looking for the right one Fragment root/parent # What's the difference in MarkLogic 11? Instead of Document Oriented, TDE Driven As many records as possible from a fragment of a single document Indexed and aware of self - but also fragment(document) To start off, all the power of MarkLogic is still there. You can still index many geospatial features in a single document. However, in normal MarkLogic practice, this foundation has been extended. When we dove into the details of the release, the feeling was that the title "Optic Geospatial" was a bit reserved in describing the power of the release. The update actually has multiple hidden gems: The heart of the release is the first bullet point extract geospatial features embedded into a document as a row. This means that you can now query for a feature and get single results per feature. Indexes available via TDE (rows, triples) (point , box , circle , linestring , polygon , complexPolygon , and a generic region) GEO library available via Optic API (just like others such as ofn, oxdmp, oxs etc) SQL and SPARQL also support geospatial features extracted from the TDE templates To further prove the point of how versatile the release is, the samples later on are mixed between SQL and Optic. # Exploring the Optic API Geospatial Features # Llamaverse With much of our testing, we try to stick with a single, familiar set of data. We then just extend the dataset as needed. For this purpose, I am using the Llamaverse geospatial data. This is familiar to those who have read the articles on GraphQL. Those examples answered questions based on relationships. We'll answer questions based on geospatial data. Prior to MarkLogic 11, we would need to store every feature in a separate document if we wanted to be able to query for each feature individually without the overhead of post-processing. # TDE Template to project features into their own rows. # Alter the content to match some TDE requirements. One interesting item points out a limitation of the TDE template grammar and features. Although you can do some complex work in creating your values in the configuration of a TDE row or triple, you cannot import modules to assist you. Furthermore, MarkLogic does not import any libraries as part of this process. The documentation is, however, very clear on what you can do and what functions are available. What this means, is that we cannot, for example, make use of the geospatial toolbox. With the rapid growth of features and fixes around the OpticAPI, I am sure that this will be addressed in the future. We represent the geospatial data as geojson. However, the underlying geospatial indexes do not readily understand this yet. The TDE engine expects cts features (such as a polygon represented as a list of cts: points or in the format of WKT (Well Known Text)). Therefore, we need to convert our coordinates into a format that is accepted. This is not necessarily impossible to do at runtime - a splits and joins and replaces. However that is a bit messy and difficult to maintain. Instead, I chose to use the tools made for that purpose and add a new version of the polygons by using a transform on ingest. The new element added is called ctsPoly2 and is just another property for each polygon. You will see that referenced in one of the TDE templates. # Pastures TDE Template We can now query the pastures directly since they have been projected into their own rows. # Llamas TDE Template Llamas may not be the most sprightly of creatures, but they are still able to move around. We can use the same technique to project the llamas into their own rows. For this, we will assume that there is a report of all llamas and their location at a given time. This is a common use-case for geospatial data. We can then use the Optic API to query for llamas in a given area at a given time. # Reporting the whereabouts of the llamas # Who is where at 10 o'clock? To answer this question, we can use the SQL interface to the database. This is a very simple query that uses the geospatial functions to find all the llamas and their location at 10:00 o'clock. Two queries are included . They are slightly different. # Full Report of Locations Of course, we may just want a report of all llamas and where they were at any time. If you look closely, you will notice one llama that is not in a pasture. We'll come back to that later. # SQL - Who's a naughty llama at 11 o' clock? Someone reported a rogue llama this morning. Our report at 11 o'clock shows a llama that has switched location. We can simply find the llama that was not in a pasture at that time. # Hmm... It was Winnie.. # Optic - Who's a naughty llama at 12 o' clock? For this example. We decided to be a bit more fluid in the way we expressed the optic query. We define the plans as we need them, use op.col and op.view-col as appropriate. Furthermore, we chose to express this result in a notExistsJoin. There are other ways in which this could have been expressed. However, this is a good example of how flexible the Optic API is. # Hmm... It was Adam.. # Mix in Optic Power with GeoSpatial Power So far, we have just done a few samples. To push things a bit further, we thought of a few more interesting questions that we could answer. # Who mixes with Whom? As a quick analysis of which Llamas may have been in contact with each other, we can do a simple exercise. For this, We chose to do a bounding box based on all the locations sampled per llama and see where those bounding boxes overlap. This is a very simple way of doing this. In a more complete example, one would have to extend the query for more than overlap (covers, is covered by, touches, etc). In addition, a bounding box is a bit generous in size. Taking time to draw a polygon from the points or perhaps overlapping circles from each point could be more precise. But for this example we will stick with the bounding box. We rely heavily on grouping aggregates to prepare our data for us. # Who moved the most As with the above example, there are other ways that one might choose to do this. As a quick example, our choice was just to find the distances between all points for each llama and find the max distance. # Which way did they go? We already know that Winnie and Adam both left the reservation (literally). It also looks like the sneaky little llamas removed their trackers. Still, we should be able to figure out which way they were heading before they left. It is technically possible for me to have gotten the answer in a different way - grouping and using op.arrayAggregate to list the points as I did in other samples and then from there taken the first two using op.call() to access fn.subsequence() to grab the last two entries. However, I did this exercise to also push the work to the D-nodes as much as possible. # Conclusion Overall, the flexibility of using the MarkLogic Geospatial features against individual projected rows or sets of triples appears to be amazingly valuable and useful. It does what it claims to do - expose the already rich geospatial feature via SQL,SPARQL and OPTIC by way of hooking into existing items and adding new geospatial datatypes to the TDE template library. However, there are a few items to keep in mind as listed below. # TDE Template Limitations As mentioned above, TDE template engine, by design, is limited by what tooling is used to process. This makes sense since template extraction at insert/runtime has a direct effect on processing time. In addition, there is no way to hook into other libraries. That means even though there may be a library available in MarkLogic that can help prepare your data to be in the format needed for indexing as a geospatial feature, it may be unreachable at index time. This leaves you with having to transform the data as a step before inserting. This is not new. However, we found that it became more apparent when we were trying to use the geospatial features. For many situations, this may never be encountered, depending on the source data used. # Performance It is never appropriate to mention performance in these types of tests. They are done on small instances and in some cases, using preview versions of code that may have extra tracing etc. However, it is important to remember: MarkLogic is a distributed database. Items projected into rows are indexed in the same forest as the document. MarkLogic balances documents across forests and nodes. Therefore, (unless you have implemented some forest rules to keep things together), your rows are across the nodes. As much as possible, work is done on the D-Nodes (resolve, filter, sort). If you write queries (SQL, Sparql, Optic) etc. and have a constant on one side of the equation (where schema-a.col.foo = 12), then life is good. However, items like where schema-a.col.foo = schema-b.col.bar then there is an inevitable situation in which data has to be transmitted across nodes to resolve the filtering and joining and self-joins. The above is not new. However, when writing queries, it is important to keep this in mind. Some of the ways that things were written above include essentially table aliases and left and right values from indexes that are no longer simple values (RegionA Overlaps Region B). The samples above are used to articulate concepts only. Many of these samples go against the grain of what is recommended for performance(actually doing many of the items mentioned above). However, they are used to show the flexibility of the Optic API and the power of the Geospatial features. Breaking them into questions where you have a constant as part of a query would help - example: (1) Who is off the reservation right now(Wendy)? and (2) from that constant (Wendy), what are her last two known positions. # State of mind Sometimes when writing queries like the above, they just flow out and work. Then I step back and look at what was written and wonder how I did that. I find Optic API to be a very powerful tool. When mixing Geospatial queries into it as well, it's important to be in the right frame of mind when deciding how to join, and most importantly, when and how to filter and group to get the results in an efficient manner. This is fun and powerful. Overall, a pleasure to test.
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"https://www.opentext.com/assets/images/HowCanWeHelp-Contact-Us.svg" ]	[]	[]	[ "" ]	null	[]	null	OpenText offers cloud-native solutions in an integrated and flexible Information Management platform to enable intelligent, connected and secure organizations.	en	/assets/images/favicon.png	OpenText	https://www.opentext.com	Business Clouds Advance your enterprise data management, data governance, and data orchestration to be AI ready. Learn more Business AI Let the machines do the work and apply AI with automation to advance your business. Learn more
correct_foundationPlace_00033	FactBench	1	78	https://www.tableau.com/fr-fr/about/press-releases/2012/marklogic%25C2%25AE-and-tableau-deliver-analytics-and-visualization	en	Salesforce	https://www.salesforce.c…0/08/default.jpg	https://www.salesforce.c…0/08/default.jpg	[ "https://www.facebook.com/tr?id=906254187153024&ev=PageView&noscript=1", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-news-explorer-header.png", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-news-explorer-header-mobile.png", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/07/Gen-AI-Data-Visualization_-5-Qs-for-Tableau-Pulses-Director-of-UX.png?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/06/MQ-Blog-List-Image-1200x675-1.png?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/05/AutoCloud_SonyHondaMobility.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/Tableau-and-Databricks-Expand-Strategic-Partnership.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/Tableau-Conference-News-Announcement.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/AI-at-Your-Fingertips_-How-Einstein-Copilot-for-Tableau-Democratizes-Data-Analysis-for-All-1.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/tab-msft.png?w=300", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-blog-cta-clouds.png", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-blog-cta-squirrel.png", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-blog-cta-left.png", "https://www.salesforce.com/news/wp-content/themes/newsroom/dist/images/bg-blog-cta-right.png", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/UntappedDataResearch.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/04/EinsteinCoPilot_Tableau.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2024/02/JohnLewis_cb6d10.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/11/Southwest.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/11/Tableau_IMDb.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/09/eToro.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/09/Einstein_Copilot_Announcement.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/06/AI_Day_PressRelease.jpg?w=300", "https://www.salesforce.com/news/wp-content/uploads/sites/3/2023/06/Yeti_CNX-1.jpg?w=300", "https://www.salesforce.com/content/dam/web/global/svg-icons/icon-cpra.svg" ]	[]	[]	[ "" ]	null	[]	2024-07-26T00:00:00	See the latest Tableau news from Salesforce including product information, thought leadership, and more.	en	https://www.salesforce.c…otext-1.png?w=32	Salesforce	https://www.salesforce.com/news/products/tableau/	By subscribing, you confirm that you agree to the processing of your personal data by Salesforce as described in the Privacy Statement
correct_foundationPlace_00033	FactBench	2	94	https://careers.ey.com/ey/job/Taguig-GDS-Consulting_Senior-Cloud-Support-Specialist-1634/1094616001/	en	GDS Consulting_Senior Cloud Support Specialist			[ "https://rmkcdn.successfactors.com/bcfdbc8a/3f2d9e7b-2820-472b-901a-c.png", "https://rmkcdn.successfactors.com/bcfdbc8a/3f2d9e7b-2820-472b-901a-c.png", "https://rmkcdn.successfactors.com/bcfdbc8a/3f2d9e7b-2820-472b-901a-c.png", "https://careers.ey.com/platform/images/shared/social/16-linkedin.png", "https://careers.ey.com/platform/images/shared/help2.png", "https://careers.ey.com/platform/images/shared/help2.png", "https://careers.ey.com/platform/images/ajax-indicator-big.gif", "https://careers.ey.com/platform/images/shared/social/16-linkedin.png", "https://careers.ey.com/platform/images/shared/help2.png", "https://careers.ey.com/platform/images/shared/help2.png", "https://careers.ey.com/platform/images/ajax-indicator-big.gif", "https://rmkcdn.successfactors.com/bcfdbc8a/688bb7d2-e818-494b-967e-0.png" ]	[]	[]	[ "Taguig GDS Consulting_Senior Cloud Support Specialist", "1634" ]	null	[]	null	Taguig GDS Consulting_Senior Cloud Support Specialist, 1634	en	//rmkcdn.successfactors.com/bcfdbc8a/013850a8-6899-42b9-89b8-9.ico		https://careers.ey.com/ey/job/Taguig-GDS-Consulting_Senior-Cloud-Support-Specialist-1634/1094616001/	At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Technical Application Support Specialist The opportunity We are building a team of L2 Technical Application Support Center in Manila. The candidate will be required to perform technical production support activities on in-scope applications. Scope of production activities include but are not limited to: Incident Management, Problem Management, Service Request Management, and Service Management. Please review the list of responsibilities and qualifications. While this is our ideal list, we will consider candidates that do not necessarily have all the qualifications but have adjacent and/or sufficient experience. Your key responsibilities · Act as a point of contact between the client and the application support center · Monitor jobs, production processes, systems availability, latency and overall system health · Create Incidents / Service Requests /Problem ticket and assign to teams · Create application incident reports · Perform initial analysis and resolution for the layers defined in scope · Establish understanding of application, platform layers defined in scope · Analyze, identify issues, conduct investigation of functional/technical bugs, service failures, operational problems and provide acceptable workaround or resolutions to identified issues/defects · Escalate further to L3 within a reasonable period with analysis and findings · Triage, resolve, and conduct RCA · Handle major incidents and join SWAT calls · Identification, classification, prioritization, and remediation of system issues · Produce reports on defect/problem reporting data · Build tools or establish processes to quickly triage issues and discover failures across the technology stack · Analyze service performance and implement adjustments to mitigate risk and/or prevent issue recurrence · Create and update support knowledge database · Lead the documentation process for overall application support Common Responsibilities and Expectations · Work and closely collaborate with client teams (business and IT) as well as 3rd party vendors on a regular basis · Provide responses to functional / technical queries, · Perform periodic application maintenance · Participate and provide support on various application migration activities · Propose and/or participate in any support team continuous improvement initiatives and implement these improvements, as needed · Understand the use of service delivery metrics · Monitor and report on regular production activities, which are subject to Service Level Agreement (SLA) or Operational Level Agreement (OLA) such as job activity, transaction processing, network activity, database activity Skills and attributes for success · Ability to work in a fast-paced production environment · Excellent analysis and troubleshooting skills · Ability to work independently and as part of a team with minimum supervision, Self-organizing and able to act under own initiative · Understanding of software development lifecycle and best practices · Excellent communication skills (written and spoken English) · Ideally has experience in working for large Financial Services client · Keen attention to detail · Have exposure to working in shifts (US Central, APAC and EMEA) and/or weekend shift/on-call support · Has successfully demonstrated domain of most skills and technologies on several relevant projects · Exposure to various job and process monitoring tools · Ability to triage, manage and resolve technical issues · Experience in utilizing and performing minor developments to existing support scripts · Participate in and be the voice of support in program increment planning and prioritisation sessions when needed · To qualify for the role, you must have: Must Have: • Intermediate level on Unix, SQL/Oracle, Java, Microservices, Tanzu / Kubernetes, Kafka, DataStage, MarkLogic • Technical Application Support (Level 2) experience (5+ years) Intermediate : has hands-on experience working on that technology, can troubleshoot w/in the technology, can read scripts or do scripting, and know the key commands • Education: Bachelor's Degree Computer related Nice to have: • Experience of DevOps engineer role is an advantage • Experience of supporting Fund Services business applications • Experience of working as part of an agile scrum team, and/or a • Exposure to script writing and basic development activity • ITIL certification • Familiarity with ServiceNow Automation and ticketing Atleast Fundamental : has knowledge but no hands on experience or has used the technology in a light way. What we look for We look for people who demonstrate drive, vision and determination and are passionate about helping our clients achieve their goals. We look for high performers, who consistently deliver quality work while continually looking for ways to improve. We want people who understand the challenges of working in a professional services environment and are focused on achieving and delivering the best for our clients. We want people who have a clear sense of personal and professional accountability and know how to build relationships by doing the right thing. What working with EY offers · Support, coaching and feedback from some of the most engaging colleagues around · Opportunities to acquire new knowledge and skills to progress your career · Engaging culture that promotes work-life balance and personal effectiveness About EY As a global leader in assurance, tax, transaction and consulting services, we’re using the finance products, expertise and systems we’ve developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible. Join us in building a better working world. Apply now. EY \| Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.
correct_foundationPlace_00033	FactBench	1	79	https://twitter.com/jennifertsauzer	en	x.com			[]	[]	[]	[ "" ]	null	[]	null			https://abs.twimg.com/re…ios.77d25eba.png	X (formerly Twitter)	null
correct_foundationPlace_00033	FactBench	1	80	https://www.businesswire.com/news/home/20190514005104/en/MarkLogic-Launches-Pharma-Research-Hub-to-Accelerate-Drug-Research-and-Results	en	MarkLogic Launches Pharma Research Hub to Accelerate Drug Research and Results	https://mms.businesswire…23/MarkLogic.jpg	https://mms.businesswire…23/MarkLogic.jpg	[ "https://www.businesswire.com/images/bwlogo_extreme.png", "https://cts.businesswire.com/ct/CT?id=bwnews&sty=20190514005104r1&sid=web02&distro=nx&lang=en", "https://mms.businesswire.com/media/20190514005104/en/721310/2/MarkLogic.jpg", "https://www.businesswire.com/images/icons/icon_search.gif", "https://www.businesswire.com/images/icons/icon-close-default.svg" ]	[]	[]	[ "" ]	null	[]	2019-05-14T12:00:00+00:00	MarkLogic Corporation, the next generation data platform provider for simplifying data integration, today announced the MarkLogic® Pharma Research Hub	en			https://www.businesswire.com/news/home/20190514005104/en/MarkLogic-Launches-Pharma-Research-Hub-to-Accelerate-Drug-Research-and-Results	SAN CARLOS, Calif.--(BUSINESS WIRE)--MarkLogic Corporation, the next generation data platform provider for simplifying data integration, today announced the MarkLogic® Pharma Research Hub to enable pharmaceutical companies to lower drug trial costs and accelerate research. This is achieved by helping researchers quickly and easily find, synthesize and share high-quality data – including, genetic, proteomic, drug, textual, binary and clinical trial data – within a single cloud service. The MarkLogic Pharma Research Hub uses machine learning and other advanced technologies including semantics, fuzzy matching, relevance ranking and rich metadata to manage, organize and retrieve information. As a fully managed cloud service, the Pharma Research Hub can be set up in minutes and ingests data 10x faster than custom-developed solutions – with zero IT management burden. “Researchers using legacy technology only access slivers of the data around them, increasing the likelihood of errors and costing valuable time that can equate to hundreds of millions of lost dollars and, more importantly, lost opportunities to change lives because drugs take so long to develop,” said Bill Fox, Vice President of Vertical Strategy Group and Chief Strategy Officer of Healthcare and Life Sciences at MarkLogic. “We offer researchers a cloud-based service to easily bring together massive amounts of data, in its original form, and understand the relationships between all that data, so they can use that data to discover life-changing drugs faster.” By working with five of the largest global pharmaceutical companies and other leaders in the healthcare ecosystem, MarkLogic has long used its Data Hub technology to help solve pharmaceutical companies’ business and data challenges by: Enabling search and visualization of relationships: View, navigate, and search the graph of connections in data by leveraging all inherent relationships. Visualizing these relationships in data, such as how a researcher is connected to institutions and peers, or how a gene, drug target and metabolic pathway are related, can lead to faster discoveries. Leveraging machine learning: Users get better search results on higher quality data. MarkLogic’s Smart Mastering feature uses machine learning to find and consolidate related and duplicate items and to construct a knowledge graph of all data. Data quality rules are applied as data is loaded. This high-quality, mastered data dramatically improves search results and enables better downstream bioinformatics and AI analysis. Loading any pharma data set: The Pharma Research Hub allows loading of any pharmaceutical information – publications, authors, drugs, genes and more – so companies can quickly access consolidated information to perform research. “Due to advances in technology that solve many data analytics challenges while also meeting the stringent regulatory requirements of the health industry, we expect substantial data management solution adoption in life science research R&D,” said Alan Louie, Life Sciences Research Director at IDC. “This new breed of industry-centric technology promises significant near-term value while also creating new best practices that will drive the industry forward for decades to come.” The Pharma Research Hub standardizes and extends the best practices from multiple customers. The underlying MarkLogic Data Hub Platform has been used to run some of the most complex businesses for nearly two decades. The platform is built for Petabytes but can also be cost-effectively scoped to small data sets. It is governed and secure from the beginning unlike data stored in a Hadoop® data lake, which can take years to build while the business waits for results. More information on the MarkLogic Pharma Research Hub is available here. About MarkLogic Data integration is one of the most complex IT challenges, and our mission is to simplify it. The MarkLogic Data Hub is a highly differentiated data platform that eliminates friction at every step of the data integration process, enabling organizations to achieve a 360º view faster than ever. By simplifying data integration, MarkLogic helps organizations gain agility, lower IT costs, and safely share their data. Organizations around the world trust MarkLogic to handle their mission-critical data, including 6 of the top 10 banks, 5 of the largest global pharmaceutical companies, 6 of the top 10 publishers, 9 of the 15 major U.S. government agencies, and many more. Headquartered in Silicon Valley, MarkLogic has offices throughout the U.S., Europe, Asia, and Australia. For more information visit www.marklogic.com. © 2019 MarkLogic Corporation. MarkLogic and the MarkLogic logo are trademarks or registered trademarks of MarkLogic Corporation in the United States and other countries. Hadoop is a registered trademark of The Apache Software Foundation. All other trademarks are the property of their respective owners.
correct_foundationPlace_00033	FactBench	1	38	https://community.tableau.com/s/idea-extension/a044T000004DonGQAS/tableau-with-marklogic	en	Tableau Community Forums			[]	[]	[]	[ "" ]	null	[]	null		en	https://www.tableau.com/favicon.ico		null
correct_foundationPlace_00033	FactBench	1	43	https://lonerganpartners.com/placements/president-ceo-gary-bloom-at-marklogic	en	President & CEO Gary Bloom at MarkLogic	https://lonerganpartners…oast-228x228.png	https://lonerganpartners…oast-228x228.png	[ "https://lonerganpartners.com/assets/website/lonergan-logo-white.png", "https://lonerganpartners.com/assets/website/search-icon-link.png", "https://lonerganpartners.com/assets/portraits/gary-bloom-master.jpg", "https://lonerganpartners.com/assets/logos/marklogic.png", "https://lonerganpartners.com/assets/portraits/gary-bloom-master.jpg", "https://lonerganpartners.com/assets/portraits/mark-lonergan-master.jpg", "https://lonerganpartners.com/assets/website/lonergan-logo-white.png" ]	[]	[]	[ "" ]	null	[]	null		en	https://lonerganpartners…h-icon-57x57.png		null	About Gary Gary Bloom is a proven technology executive who was previously the CEOand president at eMeter, which was acquired by Siemens Corporation. His background includes more than two decades of successful leadership in enterprise software. Prior to eMeter, Gary was a consultant to TPG, a leading global private investment firm. Gary was also the former vice chair and president of Symantec Corporation, where he led the company’s line of business organizations and corporate development efforts. Gary joined Symantec through the merger with Veritas Software where he was the chairman and CEO. Before joining Veritas, Gary held senior executive positions at Oracle and was in responsible for mergers and acquisitions. Gary earned his Bachelor’s Degree in Computer Science from California Polytechnic State University San Luis Obispo, where he currently serves on the President’s Cabinet and the Board of the Cal Poly Foundation. About MarkLogic MarkLogic is the ideal platform for Big Data applications designed to drive revenue, streamline operations, manage risk, and make the world safer. Organizations around the world rely on MarkLogic’s enterprise-grade technology to get to better decisions faster. MarkLogic has set new standards in scalability, enterprise-readiness, time-to-value, and innovation, giving customers an unmatched competitive edge through game-changing technology. MarkLogic 6, launched in September 2012, includes new tools for faster application development, powerful analytics and visualization widgets for greater insight, and the ability to create user-defined functions for fast and flexible analysis of huge volumes of data. MarkLogic is headquartered in Silicon Valley with field offices in Washington D.C., New York, London, Tokyo, and Austin, TX.
correct_foundationPlace_00033	FactBench	2	16	https://www.globaldata.com/company-profile/marklogic-corp/locations/	en	MarkLogic Corp Locations	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico	[ "https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 400w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 800w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 1600w", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-number-fade.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-target.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/newsletterIntro.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-report-singular-dark.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/peak.webp", "https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 400w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 800w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 1600w" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic Corp headquarters address, phone number and website information and details on other MarkLogic Corp's locations and subsidiaries.	en	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico		https://www.globaldata.com/company-profile/marklogic-corp/locations/	Have you found what you were looking for? From start-ups to market leaders, uncover what they do and how they do it.
correct_foundationPlace_00033	FactBench	1	14	https://www.4vservices.com/about/	en	4V Services	https://www.4vservices.c…/footer-logo.png	https://www.4vservices.c…/footer-logo.png	[ "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/linkedin-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/seacrh-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/sms-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/logo.png", "https://betaweb.wordsystech.com/schweizerrsg/wp-content/themes/schweizerrsg/images/search.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/searchcross.png", "https://www.4vservices.com/wp-content/uploads/2023/05/logo.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/inner-banner-bg.svg", "https://www.4vservices.com/wp-content/uploads/2023/05/about-1.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/ico-light.svg", "https://www.4vservices.com/wp-content/uploads/2023/07/client.jpg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/ico-plane.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/dots-image.svg", "https://www.4vservices.com/wp-content/uploads/2023/11/ProgressMarkLogic_PrimaryLogo.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/erxperience-icon.svg", "https://www.4vservices.com/wp-content/uploads/2023/05/msn-vsn-1.png", "https://www.4vservices.com/wp-content/uploads/2023/05/msn-vsn-2.png", "https://www.4vservices.com/wp-content/uploads/2023/07/started-working-at-MarkLogic-1.png", "https://www.4vservices.com/wp-content/uploads/2023/07/began-to-work-with-clients.png", "https://www.4vservices.com/wp-content/uploads/2023/07/first-employee.png", "https://www.4vservices.com/wp-content/uploads/2023/07/working-with-its-5th-client.png", "https://www.4vservices.com/wp-content/uploads/2023/07/official-MarkLogic-partner.png", "https://www.4vservices.com/wp-content/uploads/2023/07/10-emp.png", "https://www.4vservices.com/wp-content/uploads/2023/07/official-Semaphore-partner.png", "https://www.4vservices.com/wp-content/uploads/2023/07/conference.png", "https://www.4vservices.com/wp-content/uploads/2023/05/Profile-pic.jpg", "https://www.4vservices.com/wp-content/uploads/2024/07/Dawn-Headshot.jpg", "https://www.4vservices.com/wp-content/uploads/2023/05/4V-PamL-headShot.jpg", "https://www.4vservices.com/wp-content/uploads/2023/07/image-3.png", "https://www.4vservices.com/wp-content/uploads/2023/05/cookbook.png", "https://www.4vservices.com/wp-content/uploads/2023/07/count-1.svg", "https://www.4vservices.com/wp-content/uploads/2023/07/count-2.svg", "https://www.4vservices.com/wp-content/uploads/2023/07/count-3.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/cta-bg.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/logo.png" ]	[]	[]	[ "marklogic" ]	null	[]	null			https://www.4vservices.c…/footer-logo.png		https://www.4vservices.com/about/	Our Mission Empower Your Business with Powerful Data Management Our mission is to apply our advanced data hub solutions, deep industry expertise, and years-long experience to help clients to grow their businesses. At 4V Services, we aim to – Aggregate data to provide actionable and digestible insights Shape your business and speed up your path to growth Give your business a competitive edge in today’s digital world Our data hub solutions combine the power of a multi-model search engine, database, and semantic AI on a single platform, embedded with robust features including metadata management, government-grade security, and more! Our Vision Explore the Power and Promise of Data Today’s businesses are generating massive amounts of data. Our vision is enabling our clients to unify their data sources to power growth or savings. We envision ourselves to be your one-stop choice, when it comes to – Bringing agility in data generation and ingestion Scaling Big Data in shorter time Delivering more data reliability to transform experience We partner with our customers to provide solutions for their most pressing problems. No matter where you are on your data journey, 4V Services can help you accomplish your goals!
correct_foundationPlace_00033	FactBench	2	41	https://www.npmjs.com/package/marklogic	en	marklogic	https://static-productio…c7780fc68412.png	https://static-productio…c7780fc68412.png	[ "https://www.npmjs.com/npm-avatar/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdmF0YXJVUkwiOiJodHRwczovL3MuZ3JhdmF0YXIuY29tL2F2YXRhci85ZWQ5ODc3OTRkZDJjZjc3ZmI3ZmI1YjM5ZTVjMjk2ND9zaXplPTEwMCZkZWZhdWx0PXJldHJvIn0.lDI74oxvjsWnUGJK2A1Y3Fnt0LbOL7VZl0Z1VrPW9mw", "https://www.npmjs.com/npm-avatar/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdmF0YXJVUkwiOiJodHRwczovL3MuZ3JhdmF0YXIuY29tL2F2YXRhci9mMjcxOTljN2RjMjZmNjhlOGI0YWI4YzAyZGM5YjZiZT9zaXplPTEwMCZkZWZhdWx0PXJldHJvIn0.dQXWrx_ExgGXn28mHSpGZksllRudn9_Gh2kt0B9VwSo", "https://www.npmjs.com/npm-avatar/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdmF0YXJVUkwiOiJodHRwczovL3MuZ3JhdmF0YXIuY29tL2F2YXRhci8xNzJhNmY5ZWUwNmY3ODVlYzdiYzdhN2Y3YWUxZmJkYz9zaXplPTEwMCZkZWZhdWx0PXJldHJvIn0.4imy15P6ieQLAZLUZ3Ban8vdb9HtB3NehXgwAs17DAs", "https://www.npmjs.com/npm-avatar/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdmF0YXJVUkwiOiJodHRwczovL3MuZ3JhdmF0YXIuY29tL2F2YXRhci85ZWQ5ODc3OTRkZDJjZjc3ZmI3ZmI1YjM5ZTVjMjk2ND9zaXplPTEwMCZkZWZhdWx0PXJldHJvIn0.lDI74oxvjsWnUGJK2A1Y3Fnt0LbOL7VZl0Z1VrPW9mw", "https://www.npmjs.com/npm-avatar/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdmF0YXJVUkwiOiJodHRwczovL3MuZ3JhdmF0YXIuY29tL2F2YXRhci9iMTk0NTgyMjk5YzMyN2M1ZWJjYmViNjBkZmM4YjMzNj9zaXplPTEwMCZkZWZhdWx0PXJldHJvIn0.gPTOlAjyYb3hSd25v84pDMJhGpPqdqmfRTKvfA1b2iQ" ]	[]	[]	[ "marklogic", "nosql", "database", "dbms", "search", "query", "json", "xml", "http", "xquery", "xpath" ]	null	[]	2024-04-26T22:51:29.462000+00:00	The official MarkLogic Node.js client API.. Latest version: 3.4.0, last published: 3 months ago. Start using marklogic in your project by running `npm i marklogic`. There are 20 other projects in the npm registry using marklogic.	en	https://static-productio…7863c94673a4.png	npm	https://www.npmjs.com/package/marklogic	The MarkLogic Node.js Client API provides access to the MarkLogic database from Node.js applications. Writing, reading, patching, and deleting documents in JSON, XML, text, or binary formats Querying over documents including parsing string queries, extracting properties, and calculating facets Projecting tuples (like table rows) out of documents Single transactions and multi-statement transactions for database changes Writing, reading, and deleting graphs and executing SPARQL queries over graphs Extending the built-in services or evaluating or invoking your own JavaScript or XQuery on the server Basic, digest, certificate, Kerberos, and SAML authentication Import libraries as JavaScript mjs modules Data Services First - MarkLogic's support for microservices Optic query DSL, document matching, relevance, multiple groups Generate query based views, redaction on rows Data Movement SDK - move large amounts of data into, out of, or within a MarkLogic cluster You can install the marklogic package as a dependency for your Node.js project using npm: npm install marklogic --save For Windows OS please use the below for Node Client 2.9.1: npm install marklogic --save --ignore-scripts With the marklogic package installed, the following inserts two documents in a collection into the Documents database using MarkLogic's built-in REST server at port 8000: Node.js Client API Documentation Node.js Application Developer's Guide MarkLogic Training for the Node.js Client API The Node.js Client API ships with code examples to supplement the examples in the online resources. To run the examples, follow the instructions here: examples/1readme.txt After installing the project dependencies (including the gulp build system), you can build the reference documentation locally from the root directory of the marklogic package: npm run doc The documentation is generated in a doc subdirectory. The documentation can also be accessed online here.
correct_foundationPlace_00033	FactBench	2	57	https://www.datadoghq.com/blog/monitor-marklogic-with-datadog/	en	Monitor MarkLogic with Datadog	https://imgix.datadoghq.…rop&w=1200&h=630	https://imgix.datadoghq.…rop&w=1200&h=630	[ "https://imgix.datadoghq.com/img/dd_logo_n_70x75.png?ch=Width,DPR&fit=max&auto=format&w=70&h=75", "https://imgix.datadoghq.com/img/dd-logo-n-200.png?ch=Width,DPR&fit=max&auto=format&h=14&auto=format&w=807", "https://imgix.datadoghq.com/img/datadog_rbg_n_2x.png?fm=png&auto=format&lossless=1", "https://imgix.datadoghq.com/img/blog/_authors/paulgottschling.jpg?auto=format&w=60&h=60 1x, https://imgix.datadoghq.com/img/blog/_authors/paulgottschling.jpg?auto=format&w=60&h=60&dpr=2 2x", "https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/hero-final.png?w=1280&auto=format&q=80&fit=max&lossless=1&dpr=1 1x, https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/hero-final.png?w=1280&auto=format&q=80&fit=max&lossless=1&dpr=2 2x, https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/hero-final.png?w=1280&auto=format&q=80&fit=max&lossless=1&dpr=3 3x", "https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=422& 1x, https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=422&dpr=2 2x", "https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/oob-dash.png?auto=format&fit=max&w=847", "https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/storage-metrics.png?auto=format&fit=max&w=847", "https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/client-metrics.png?auto=format&fit=max&w=847", "https://imgix.datadoghq.com/img/blog/monitor-marklogic-with-datadog/log-search.png?auto=format&fit=max&w=847", "https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=120& 1x, https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=120&dpr=2 2x", "https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=422& 1x, https://imgix.datadoghq.com/img/further-reading/ThumbnailMMI2023.png?auto=format&w=422&dpr=2 2x" ]	[]	[]	[ "" ]	null	[ "Paul Gottschling" ]	2020-11-13T00:00:00+00:00	Keep your distributed storage layer in good shape with Datadog's integration.	en	https://imgix.datadoghq.…e-touch-icon.png	Datadog	https://www.datadoghq.com/blog/monitor-marklogic-with-datadog/	MarkLogic is a multi-model NoSQL database with support for queries across XML and JSON documents (including geospatial data), binary data, and semantic triples—as well as full-text searches—plus a variety of interfaces and storage layers. Customers include large organizations like Airbus, the BBC, and the U.S. Department of Defense. Because MarkLogic can process terabytes of data across hundreds of clustered nodes, maintaining a deployment is a complex business. Datadog’s integration for MarkLogic gives you the visibility you need to identify performance issues and tune your deployments more effectively. As soon as you enable the integration, you can use an out-of-the-box dashboard to start monitoring MarkLogic right away. Monitor your storage performance MarkLogic is designed to process massive amounts of data, but misconfigured clusters can bog down performance. Datadog’s MarkLogic integration helps you ensure that data travels from your storage layer to clients as quickly as possible. MarkLogic stores data in forests, groups of XML, JSON, text, or binary documents associated with a particular file system. Administrators attach forests to a single database, which carries out read and write operations against the forests while executing queries. Forest-backed data is compressed and stored in fragments. MarkLogic servers responsible for managing forests, called Data Nodes, send these fragments over the network to specialized servers, called Evaluator Nodes, that expand the fragments in order to serve queries. Data Nodes store fragments in the compressed tree cache, which prevents them from having to read data directly from disk (this is slower and has the potential for lock contention if a document is being updated). You can track read query throughput by summing the metrics marklogic.hosts.query_read_rate and marklogic.hosts.large_read_rate. (Read metrics for other operations, such as backups and merges, are also available; see our documentation for details.) If read query throughput is increasing while the hit rate for the compressed tree cache (marklogic.forests.compressed_tree_cache_hit_rate) is decreasing, it’s likely that the cache is not large enough to handle the new queries—consider adding memory to the cache. Datadog also tracks hit rates for other MarkLogic caches, such as the list cache and expanded tree cache, so you can tune your queries more effectively. Understand network activity MarkLogic nodes need to communicate with clients and other nodes within a distributed cluster. Datadog can help you detect traffic spikes and connection failures in your MarkLogic deployment. MarkLogic nodes communicate via the XML Data Query Protocol (XDQP), and use a heartbeat to evict unresponsive nodes from the cluster. If some nodes get evicted, the remaining healthy nodes could become overloaded with query traffic, causing a cascading failure. You can track XDQP throughput using metrics following the pattern marklogic.hosts.xdqp_(client\|server)_(send\|receive)_rate. Group this metric by the marklogic_host_name tag to see if spikes or losses in traffic are particularly acute for certain hosts. If a spike in XDQP throughput correlates with CPU saturation across your nodes—or begins to drop off—you can take steps to protect your cluster. Client applications can query a MarkLogic database using HTTP, ODBC, XDBC, or WebDAV at endpoints called App Servers. Use marklogic.requests.total_requests to track active requests to MarkLogic App Servers, and filter this metric by the server_name tag to monitor demand on a specific server. (You can configure resource filters to enable tagging MarkLogic metrics by the names of specific forests, databases, hosts, and servers.) If you suspect that high request traffic is causing resource saturation issues in your MarkLogic cluster, consider setting limits on concurrent requests to your App Servers or adding more evaluator nodes. Stay on top of errors Datadog’s MarkLogic integration helps you quickly detect and analyze trends in error logs. A built-in log-processing pipeline automatically enriches your MarkLogic logs with facets, so you can group and filter error logs to identify trends. For example, you can group App Server error logs by URL path to see if a specific endpoint is behind the problem, or group by database operation to see if particular types of queries are causing internal error messages. You’ll want to take action as soon as possible if MarkLogic is emitting error logs more frequently than usual—Datadog enables you to create alerts that will automatically notify your team when this occurs, so you can quickly start troubleshooting. Unify your MarkLogic monitoring
correct_foundationPlace_00033	FactBench	1	1	https://www.progress.com/marklogic	en	Database Platform to Simplify Complex Data	https://d117h1jjiq768j.c…fvrsn=146272b8_1	https://d117h1jjiq768j.c…fvrsn=146272b8_1	[ "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/marklogic-hero-hex-top.svg?sfvrsn=76c86e33_5", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/marklogic-overview_illustration.svg?sfvrsn=2da706d7_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/bbc-logo.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/anb-ampro-logo.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/healthcare-gov-logo.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/nbc.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/airbus.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/merck.svg", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/ai-data-visualization.png?sfvrsn=2c0b3aa1_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/marklogic-overview-hex_crop.svg?sfvrsn=135ba97d_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/istrategic_initiatives_llustration.svg?sfvrsn=a9768859_8", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/data_architectures_illustration.svg?sfvrsn=9ef4cb53_6", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/industries_illustration.svg?sfvrsn=9f10dc3d_6", "https://d117h1jjiq768j.cloudfront.net/images/default-source/semaphore/success-stories-semaphore/broadridge_cs_301.jpg?sfvrsn=beb1b19d_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/default-album/progress-album/documents-album/papers-album/unleash-the-power-of-generative-ai-list.png?sfvrsn=882e851d_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/resource-center/ebook/ebook-2-thumbnail.svg?v=2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/blogs/marklogic/knowledge-graph-isnt-enough-drips-1600x745.jpg?sfvrsn=57285327_6", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/marklogic-overview-prefooter-bg.svg?sfvrsn=8a31df87_6" ]	[]	[]	[ "" ]	null	[]	null	Solve your most complex data challenges and unlock more value with the MarkLogic data platform.	en	/favicon.ico?v=2	Progress.com	https://www.progress.com/marklogic	Data Platform Accelerate data, AI and analytics projects, manage costs and deliver enterprise growth with the Progress Data Platform. Digital Experience Real solutions for your organization and end users built with best of breed offerings, configured to be flexible and scalable with you. Infrastructure Management Progress infrastructure management products speed the time and reduce the effort required to manage your network, applications and underlying infrastructure. Federal Solutions Software products and services for federal government, defense and public sector.
correct_foundationPlace_00033	FactBench	1	55	https://azuremarketplace.microsoft.com/en-us/marketplace/apps/marklogic.marklogic-enterprise-11%3Ftab%3Doverview	en	Microsoft Azure Marketplace	https://azuremarketplace.microsoft.com/favicon.ico	https://azuremarketplace.microsoft.com/favicon.ico	[]	[]	[]	[ "" ]	null	[]	null		en	/favicon.ico		https://azuremarketplace.microsoft.com/en-us/marketplace/apps/marklogic.marklogic-enterprise-11?tab=overview	MarkLogic Server is the agile, scalable, and secure foundation of the MarkLogic Data Platform. A multi-model database with a wide array of enterprise-level data integration and management features, MarkLogic helps you create value from complex data—faster. MarkLogic Server natively stores JSON, XML, text, geospatial, and semantic data in a single, unified data platform. This ability to store and query a variety of data models provides unprecedented flexibility and agility when integrating data from silos. MarkLogic is the best, most comprehensive database to power an enterprise data platform. MarkLogic Server is built to securely integrate data, track it through the integration process, and safely share in it in its curated form. Meet business-critical goals and accelerate innovation with MarkLogic. Highlights:
correct_foundationPlace_00033	FactBench	2	77	https://blog.davidcassel.net/marklogic-for-node-js-developers/welcome-to-marklogic/	en	Welcome to MarkLogic	http://blog.davidcassel.net/wp-content/uploads/2015/10/QueryConsole.png		[ "http://blog.davidcassel.net/wp-content/uploads/2015/10/QueryConsole.png" ]	[]	[]	[ "" ]	null	[]	null		en	https://blog.davidcassel.net/wp-content/themes/sunset_castle_theme/favicon.ico		https://blog.davidcassel.net/marklogic-for-node-js-developers/welcome-to-marklogic/	This is part of a draft ofÂ MarkLogic 8 for Node.js Developers. Incomplete sections are [marked with brackets]. MarkLogic provides developers with a powerful set of tools for solving complex problems stemming from the volume, velocity, and variety of Big Data problems. This book will introduce the concepts of MarkLogic and illustrate them using a substantial application. Why MarkLogic? MarkLogic was founded in 2001 to solve problems related to what we’ve come to know as Big Data. This term is used to describe the three-way challenge of data with high volume, variety, and velocity. High volume refers to large quantities of data, often in the terabyte or petabyte scale. MarkLogic addresses this problem partly by scaling out, allowing the use of commodity hardware to expand capacity, but also by effective use of indexes and map/reduce approaches to provide fast responses even as the volume of content grows. The document nature of MarkLogic storage helps achieve this ability to scale. Data with a lot of variety poses a substantial problem for technologies that require a schema design before data can be ingested. MarkLogic’s schema-agnostic approach allows the presence of data with different schemas side-by-side in the same database, allowing developers to focus on how to make use of the data in an application, rather than spending a lot of time to figure out how to represent it. The velocity of change is a similar type of problem. Designing a schema for a relational database often requires a significant amount of work. Designers try to anticipate change, but when changes happen a lot of effort is needed. Furthermore, a change in a relational schema will touch every row in the affected tables. With MarkLogic’s document orientation, work resulting from schema changes focuses on the application itself. In many cases, a schema change only affects a subset of the documents, and only these will need to be updated. These differences and more are addressed in more detail in the Data Modeling chapter. MarkLogic has been working with customers in Media, Publishing, Public Sector, Financial Services, and other industries, starting with the version 1 release in 2003. Concepts MarkLogic is an enterprise-class NoSQL information store and search engine. There’s a lot contained in that sentence — let’s break it down into pieces. Enterprise Class MarkLogic supports ACID transactions, government-grade security, high availability, and disaster relief. These are all features you’d expect from a database that large organizations trust with their critical data. Appendix A addresses how MarkLogic supports ACID transactions. The Security chapter discusses the role-based security approach used within MarkLogic. NoSQL “NoSQL” databases emerged in response to the need for new ways to manage data, as many projects struggled to meet their needs with traditional relational databases. The term refers to SQL, the query language used to interact with relational databases. Note that some NoSQL databases, including MarkLogic, do support some degree of interaction through SQL, leading some to expand “NoSQL” to “Not-Only SQL”. Information Store MarkLogic provides a document store and a triple store, providing tremendous flexibility in the types of data it can handle. We’ll explore what each of these means to you as a developer in the coming chapters. Search Engine In addition to storing data, MarkLogic provides a powerful set of features to search that data. More than 30 types of indexes power this capability, leading to very fast search results. The Search chapter describes this topic in detail. MarkLogic Application Architecture Applications are typically built with three tiers: the database, application, and presentation tiers. Each of these layers has its role, though the lines between them can be blurry. Database Tier MarkLogic provides the database tier, taking responsibility for storing and retrieving data with consistency and durability. Interaction between the application tier and a MarkLogic database goes through the MarkLogic REST API, Java Client API, Node.js Client API, or through custom-built endpoints. Using one of the provided APIs means broad out-of-the-box capabilities, in addition to a mechanism to extend that API using Server-side JavaScript or XQuery. In this book, we’ll focus on the Node.js Client API. Middle Tier The middle tier defines the interface that will be used by the presentation tier, controlling access to the database’s API. Business logic is often implemented here, though MarkLogic’s support of complex processing in the database means it is sometimes helpful to move the code close to the data. The middle tier is also a good place for code that interacts with third-party systems, such as using social networks for logging in or resizing uploaded images. In this book, the middle tier will be implemented with Node.js. Presentation Tier The presentation tier is what the end-user actually sees. This may take the form of a web page viewed in a browser, a mobile app, or a desktop application. The presentation tier will send messages to the middle tier based on the user’s actions. Working With MarkLogic MarkLogic offers a variety of ways to interact with the database. Each of these goes through an application server, which is also included in MarkLogic. In this book, we will work with HTTP application servers, but there are also XDBC, ODBC, and WebDAV application servers. For more information about these, see the MarkLogic Administrator’s Guide. Query Console One of the applications that MarkLogic ships with is call Query Console. This provides a way to run ad hoc queries using Server-side JavaScript, XQuery, SPARQL, or SQL. JavaScript and XQuery are used to query and update a database and to transform data. SPARQL is used for Semantic queries and is covered in the Semantics Chapter. The SQL view interface is primarily for connecting Business Intelligence tools and is not covered in this book. See MarkLogic’s SQL Data Modeling Guide for more information. If you have installed and started up MarkLogic, Query Console should be running. Point your browser to http://localhost:8000/qconsole/. You will see something like Figure 1. Figure 1: Query Console Query Console provides buffers, such as “Query 1”, where you can type JavaScript expressions, click the Run button, and see the results in the lower section. Workspaces are listed on the right side of the screen. Each Workspace consists of a set of buffers. Figure 1 shows Query 1 in the default Workspace. Developers can use Query Console to experiment with code, figuring out the right way to express a query or other task. Query Console can also be used to make small updates to a database. [More about QC. More information in the QC Guide.] Node.js Client API For the technology stack used in this book, JavaScript is the language of choice throughout the tiers. MarkLogic uses the term “Server-side JavaScript” to refer to JavaScript running on the V8 engine embedded within MarkLogic, so I’ll use that term the same way. Node.js also uses JavaScript code on the server, but in a different tier. The language is the same, though there are some important usage differences. Node.js is optimized for I/O heavy applications. MarkLogic is the perfect companion for Node, as much of the analytical processing and data transformations can be handled in the database itself. The programming model used in Node is asynchronous. The application makes a request then works on something else while waiting for the request to complete. The MarkLogic Node.js Client API supports callbacks, Promises, and streaming as asynchronous approaches. MarkLogic uses JavaScript to extend the built-in capabilities, but uses a synchronous interface to do so. While Node.js is single-threaded, MarkLogic application servers use multiple threads to handle multiple client requests. MarkLogic also uses lazy evaluation to increase parallelism in its processing. Documents are stored in MarkLogic as JSON, XML, text, or binary; the choice among these options is discussed in the chapter on Data Modeling. Applications commonly use more than one. Using JSON documents gives the advantages of not needing to transform them, but there are advantages to XML as well, particularly for HTML or text content. Listing 1: Example of inserting a JSON document and reading it back Listing 1 demonstrates the Node Client API, saving a document to the database and reading it back. Line 1 loads the Node module, using a typical Node “require” statement. Lines 3-8 establish a connection to the database, using the built-in App-Services application server on port 8000 and the admin user[1]. Each application server is configured to use a specific content database. By default, the App-Services application server points to the Documents database, so that is where the document will be loaded. Line 10 specifies the document’s URI, which uniquely identifies the document within the database. Lines 12-18 write the document into the database. The write function takes a document descriptor that specifies the URI and the content of the new document. In this case, the document is itself a simple JSON object, with the title and author of this book. The Node Client API offers choices for handling responses. Lines 18-25 demonstrate the Promise pattern. Other choices are Callback, Object Mode Streaming, and Chunked Mode Streaming. These options are discussed in the Key Concepts and Conventions section of the Node.js Application Programmer’s Guide published by MarkLogic. This book will focus on the Promise pattern. [Discuss error handling (maybe not here). For each method, describe and show how to catch errors and what type of errors get caught.] Samplestack The goal of this book is to show you how to build MarkLogic applications. You will learn both by reading about concepts and seeing them put into practice in Samplestack, an implementation of the MarkLogic Reference Architecture. Samplestack is based on the popular question-and-answer website Stack Overflow. Stack Overflow provides data downloads, which were used to seed the Samplestack data set. Samplestack modifies the original application in a few ways, in order to illustrate MarkLogic concepts. Setup To follow along, you can set up Samplestack on your own computer. [Describe how to install and run Samplestack.] Features Each of the features in Samplestack was selected to illustrate important concepts in MarkLogic. In the last section, you saw how to install and run Samplestack. [add Samplestack screenshot] Figure 2: Samplestack’s initial view After starting up Samplestack, point your browser to http://localhost:3000 and youâ€™ll see the initial view. Samplestack is a question-and-answer site. Logged-in users can ask questions, answer them, and comment or vote on questions and answers. When the asker of a question sees an answer that satisfies his or her need, the asker can accept that answer, causing it to be displayed above other answers. Guest users can see questions that have accepted answers and can search by terms, tags, date, or user. Getting votes and having answers accepted influences a user’s reputation. Each feature in Samplestack was selected to illustrate some aspect of MarkLogic. Text and facet search: MarkLogic indexes the text from all documents, allowing fast searches for words and phrases. Samplestack also provides facets on dates and tags, allowing the user to explore content. User records and Question documents: the content of Samplestackâ€™s database consists of two types of documents. The chapter on Loading and Modeling Data discusses the thought process for modeling data this way. Users and Roles: only logged-in users may use features that change the content of the database. Guest users only see questions that have accepted answers. The Security chapter shows how this works. Voting: A vote not only affects the answer to which it is applied; it also changes the reputation of the person who wrote the answer. A vote triggers a multi-document update performed in a single transaction to ensure data integrity. Related tags: MarkLogic is a semantic triple store, in addition to being a document store. This feature lets users browse by related tags to find questions that might be of interest. The rest of this book will use Samplestack features to illustrate important concepts you will use in building your own applications. Additional Resources MarkLogic University on demand video: “Introduction to MarkLogic”. This 24-minute video introduces MarkLogic at a high level. MarkLogic University instructor-led training: “MarkLogic Fundamentals”. This one-day course goes deeper to introduce MarkLogic’s use cases and capabilities. Samplestack GitHub repository: On GitHub, you can request new features, report bugs, and explore the source code. MarkLogic University on demand video: “Samplestack Demo“. Get a preview of what this application does.
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Here's how Datavid leverages the technology.	en	https://datavid.com/hubf…id%20favicon.png		https://datavid.com/partners/marklogic	About the partner MarkLogic is a long-standing leader in the NoSQL database space, specifically for its deep search capabilities, but also its multi-model database—enabling semantic functionality at scale. Datavid's comment Datavid's relationship with MarkLogic runs deep throughout the entire culture. It's where we began our journey, and it continues to be a key partner for our respective growth.
correct_foundationPlace_00033	FactBench	2	36	https://www.iri.com/blog/migration/data-migration/using-marklogic-data-in-iri-voracity/	en	Using MarkLogic Data in IRI Voracity	https://www.iri.com/blog…gic-voracity.png	https://www.iri.com/blog…gic-voracity.png	[ "https://www.iri.com/blog/wp-content/uploads/2019/02/iri-logo-total-data-management-small-1.png", "https://www.iri.com/blog/wp-content/uploads/2016/06/marklogic-voracity-618x350.png", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-0.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-1.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-3.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-4.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-5.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-6.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-7.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-8.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-9.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-10.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-11.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-12.jpg", "http://www.iri.com/blog/wp-content/uploads/2016/06/t_Using-MarkLogic-Data-in-IRI-Voracity-Chaitail-Mitra-13.jpg", "https://www.iri.com/blog/wp-content/uploads/2024/07/Featured-image-Oracle-TLS-support-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2014/06/Textual-ETL-featured-image-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/05/Featured-image-SQL-SSL-Connection-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/04/Bloor-Research-on-IRI-data-classification-featured-image-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/02/IRI-Voracity-and-Data-Fabric-banner-350x250.jpg", "https://www.iri.com/blog/wp-content/uploads/2024/01/Featured-image-DarkShield-PII-Discovery-Masking-Charts-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/01/data-class-mapping-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/01/schema-data-class-search-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/01/Finding-masking-PII-RDB-wizard-featured-image-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2024/01/Featured-Image-Files-Wizard-350x300.png", "https://www.iri.com/blog/wp-content/uploads/2023/12/Featured-image-for-import-data-classes-350x300.png" ]	[]	[]	[ "" ]	null	[ "Chaitali Mitra" ]	2016-06-20T17:12:25+00:00	The IRI Voracity data management platform now supports the MarkLogic NoSQL database as a source for structured data discovery (classification, profiling, and search), integration (ETL, CDC, SCD), migration (conversion and replication , governance (data cleansing and masking), and analytic (reporting and wrangling) jobs. In this article, I explain how to set up the MarkLogic server for SQL operations,Read More	en		IRI	https://www.iri.com/blog/migration/data-migration/using-marklogic-data-in-iri-voracity/	The IRI Voracity data management platform now supports the MarkLogic NoSQL database as a source for structured data discovery (classification, profiling, and search), integration (ETL, CDC, SCD), migration (conversion and replication , governance (data cleansing and masking), and analytic (reporting and wrangling) jobs. In this article, I explain how to set up the MarkLogic server for SQL operations, and configure Voracity to source MarkLogic data via ODBC in IRI Workbench. MarkLogic Server is the Enterprise NoSQL Database that combines database internals, search-style indexing, and application server behaviors. It uses XML documents as its data model, and stores the documents within a fully ACID compliant transactional repository. It indexes the words and values from each of the loaded documents, as well as the document structure. And, because of its unique Universal Index, MarkLogic doesn’t require advance knowledge of the document structure (its “schema”), nor complete adherence to a particular schema. Through its application server capabilities, it is programmable and extensible. To set up the MarkLogic server for ODBC access, I need to create: a SQL database range indexes for the database data fields an ODBC apps server Set up MarkLogic Server Install MarkLogic on your network and reach it through the browser in IRI Workbench for convenience. Select Windows=>Show View=>Other=>Internal Web browser and navigate to http://hostname:8001: Configure ODBC Server & Create the Database To create a SQL-ready database in MarkLogic, the first Configure tab step is to create a “forest” and attach it to the database, which I named SQLData1. I then created an ODBC Server (shown below, via Groups, Apps Server) named SQL with Port number 5432. In the modules field, select (file system) to store MarkLogic documents, and in the database field, select the SQLData1 database we created. Click OK to save these settings. Next, click to expand Databases in the explorer pane, and under SQLData1, create range element indexes to define each column name and data type for use in multiple tables within a schema we will later call “main”: Creating Tables (Views) in MarkLogic Given that we have previously defined columns for use, we can assign them to a new schema which will have a series of defined tables or views. To create the schema, use a Curl command like this: curl -X POST –anyauth –user admin:admin –header “Content-Type:application/json” -d ‘{“view-schema-name”: “main”}’ http://localhost:8002/manage/v2/databases/SQLData1/view-schemas/?format=json Once I create the schema ‘main’ I will create a view called ‘emps’, which contain some of the previously defined range element index IDs (or columns); e.g., ‘firstname’, ‘lastname’, and ‘employeeid’ range indexes. Employeeid uses the integer data type, while FirstName, LastName use a string. Curl Code in Cygwin prompt Through these views, SQL inserts and queries via ODBC will work in MarkLogic’s Query Console (below), and thus, operations on this data in IRI Voracity as well. For more detailed instructions in this area, refer to https://docs.marklogic.com/guide/sql/setup. Loading & displaying data in MarkLogic Query Console In the IRI Workbench internal browser, I can access the MarkLogic Query Console to do ad hoc queries, insert XML or JSON documents, or RDF Triples. In this case, I will use it to enter (load / insert) the actual data elements into my now SQL/ODBC-ready view, emps, via JavaScript. Each row is stored as a JSON document in this case, and can be queried with SQL syntax. ODBC connection in IRI Workbench Once the backend DB is configured, we must configure its ODBC driver for use with Voracity. From the IRI Workbench, I click on the toolbar’s IRI icon, and select Data Connection Registry. From there, click add: From the ODBC Data Source Administrator window, use the System DSN tab and Configure … to enter the connection parameters to MarkLogic. In the MarkLogic SQL ODBC Driver Setup window, enter the database name we created (in this case SQLData1). The server name is localhost, and username and password match what’s in use with the MarkLogic server and port (5432). Test and save the connection. Retrieve data from MarkLogic (View) & Load in Oracle I next need to create an IRI data definition file (DDF) to make use of the MarkLogic data in each view. To do this in the IRI Workbench GUI for Voracity (or other IRI products using DDF), I will use the Import Metadata Wizard. First, I create a New IRI Project in the Workbench Project Explorer to hold my work: Next, from the IRI Menu=> Select Import Table metadata. Select the Data Source Name (DSN) “MarkLogicSQL” and the table “main.emps”: The resulting DDF file is shown below; note that my connection to MarkLogic must remain open while I’m interfacing with it: This DDF is now available for use in any IRI job script sourcing this table. I will use it in my sort and mask application below. From the CoSort toolbar menu (stopwatch icon), select New Sort Job. After naming my job script, I am taken to the data source specification. I locate my ODBC source for the MarkLogic table, and then select Add Existing Metadata to provide the necessary field layouts for the CoSort program. Voracity uses SortCL to manipulate, mask, and report on MarkLogic and other ODBC and file-based data sources. I can then specify one or more sort keys: In the next screen I define and format my target(s), where I also specified a redaction rule to mask sensitive portions of the column values on output: I also redacted the FIRSTNAME and LASTNAME column values with the replace_chars(FIRSTNAME, “*”). Protection rule. See this video on how to use IRI Workbench dialogs (or wizards) to redact data and otherwise mask sensitive data in your target fields. The code and the output produced in IRI Workbench The job produced in the wizard connects to MarkLogic, sorts and masks the data in the main.emps view, and sends the output to both an Oracle DB and flat-file (standard output) target, both shown below:
correct_foundationPlace_00033	FactBench	1	63	https://www.dbta.com/Editorial/News-Flashes/MarkLogic-Acquires-Leading-Metadata-Management-Provider-Smartlogic-150235.aspx	en	MarkLogic Acquires Leading Metadata Management Provider Smartlogic	https://www.dbta.com/ima…al-logo-2019.png	https://www.dbta.com/ima…al-logo-2019.png	[ "https://dzceab466r34n.cloudfront.net/Images/SocialMediaImages/twitter-x-logo.png", "https://www.dbta.com/images/icon_linkedin.png", "https://www.dbta.com/images/icon_facebook.png", "https://www.dbta.com/Images/Default.aspx?ImageID=25089&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25089&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25086&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25085&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25070&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25068&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25052&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24832&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24669&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24550&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24468&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=23938&maxWidth=125&canvas=125x165", "https://www.dbta.com/images/logo_footer.png", "https://www.dbta.com/images/partnerlogos/idug-logo-sm-on-2.gif", "https://www.dbta.com/images/partnerlogos/iiug-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/isug-tech-7_sm.png", "https://www.dbta.com/images/partnerlogos/oaug-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/pass-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/share_logo_small-new.png", "https://www.dbta.com/images/partnerlogos/ukoug-logo-sm-on.jpg", "https://www.dbta.com/images/partnerlogos/DBTA-Media-Partner-DSI-40.png" ]	[]	[]	[ "" ]	null	[]	2021-11-23T00:00:00	MarkLogic, the provider of a data management platform that runs on a NoSQL foundation and a portfolio company of Vector Capital, has acquired Smartlogic, a metadata management solutions and semantic AI technology provider. As part of the transaction, Smartlogic's founder and CEO, Jeremy Bentley, as well as other members of the senior management team, will join the MarkLogic executive team.	en		Database Trends and Applications	https://www.dbta.com/Editorial/News-Flashes/MarkLogic-Acquires-Leading-Metadata-Management-Provider-Smartlogic-150235.aspx	MarkLogic, the provider of a data management platform that runs on a NoSQL foundation and a portfolio company of Vector Capital, has acquired Smartlogic, a metadata management solutions and semantic AI technology provider. As part of the transaction, Smartlogic’s founder and CEO, Jeremy Bentley, as well as other members of the senior management team, will join the MarkLogic executive team. Financial terms of the transaction were not disclosed. “Enterprises are facing significantly more complex data challenges than ever before,” said Jeff Casale, CEO of MarkLogic. “By acquiring and integrating with Smartlogic, a best-in-class metadata and AI platform, we provide our customers with the tools to more easily unlock the enormous value embedded in human-generated content. We’re very excited to work with Jeremy and his talented team as we grow the business and deliver better outcomes for our customers.” “Smartlogic unlocks the value in important data sets many enterprises rely on by leveraging sophisticated semantic AI to enable better decision making,” added Stephen Goodman, a Principal at Vector Capital. “Smartlogic’s ability to deliver actionable intelligence is complementary with MarkLogic’s powerful offerings and we are excited to deliver a more complete and informed perspective to customers through this combination.”
correct_foundationPlace_00033	FactBench	2	61	https://diginomica.com/battling-pole-position-nosql-market	en	MarkLogic: Battling for pole position in the NoSQL market	https://diginomica.com/s…om-MarkLogic.png	https://diginomica.com/s…om-MarkLogic.png	[ "https://diginomica.com/themes/custom/diginomica_theme/logo.png?v=20220726", "https://diginomica.com/sites/default/files/styles/square_28/public/default_images/default-avatar.png.webp?itok=lR0dr6oT 28w, /sites/default/files/styles/square_56/public/default_images/default-avatar.png.webp?itok=3R97vG49 56w", "https://diginomica.com/sites/default/files/styles/scaled_370/public/images/2014-05/GaryBloom-MarkLogic.png.webp?itok=alDhNhHx 370w, /sites/default/files/styles/scaled_740/public/images/2014-05/GaryBloom-MarkLogic.png.webp?itok=Oczff16X 449w", "https://diginomica.com/themes/custom/diginomica_theme/images/pwa/placeholder.jpg" ]	[]	[]	[ "NoSQL" ]	null	[ "Kenny MacIver" ]	2014-05-01T02:38:17-07:00	MarkLogic CEO Gary Bloom talks to Diginomica about size, enterprise-level table stakes and the lead generation engine provided by his open source database...	en	/themes/custom/diginomica_theme/images/favicon/apple-touch-icon.png	diginomica	https://diginomica.com/battling-pole-position-nosql-market	On the surface, there’s a striking similarity between today’s red-hot NoSQL database software market and what was happening in the relational database management systems market in the early 1990s. Back then, analysts were giving products such as Informix, Ingres, Sybase and Rdb as much chance of becoming the clear leader of the then-nascent sector as SQL Server, DB2, and the ultimate winner Oracle, which today commands around 50% of the market (according to Gartner), with RDBMS revenues greater than all the others in the top five combined. It is those kinds of numbers and the chance to mold the next Oracle that brought Gary Bloom (himself a 14-year veteran of the Redwood Shores, CA software giant) back into the database fray in 2012 after a dozen years in storage, security and smart meter management software. And even at this early stage of the NoSQL market, there are some signs his bet may not have been misjudged. “It has been at least 25 years since there was a transition of database technology, and I looked at [the current market dynamics] and concluded that if Oracle became this dominant force by focusing on managing about 20% of the data [the fifth that’s structured], what could I do with the next generation of database technology that deals with the other 80% of the world’s data that is highly unstructured.” Coming from a purely enterprise software background, his view on the NoSQL field was mixed: while recognizing it as the next generation of database technology, none of the players took the fundamentals of enterprise software seriously — except one. “At the time, MarkLogic was the only company paying attention to enterprise-class issues: the notion that says you have to have security, high availability and transactional consistency, and all these things that real data centers demand if they are going to run your technology. Just because you’re thinking of changing database technologies (and there are lots of advantages to doing so) doesn't mean you throw away all of the requirements for things like data protection and proper back ups that recover after a disk failure. Those are table stakes qualities,” says Bloom, who became CEO of the company two years ago. That differentiation aside, it didn’t escape his attention either that MarkLogic was already the biggest player in a young, crowded market. According to market analysts at Wikibon , MarkLogic led the $542 million market for NoSQL and Hadoop software and services in 2012 with a 13% share, ahead of Cloudera’s 10%, IBM with 9% (mostly from services) and MongoDB with 7%. Wikibon reckons the market doubled last year and will surge by another 70% this year to reach $1.7 billion, while rising at a 45% CAGR though to 2017, with NoSQL the slightly larger of the two segments. Content driver Unlike many of its rivals, MarkLogic’s pole position draws on a historical base that dates back to 2001. Its core product fuses together database, search-style indexing and application server operations, using XML documents as its data model. That naturally meant early conquests were in document-centric industries like content publishing. As Bloom points out, its technology offered the capability to manage documents based on their actual content by indexing their words and values, rather than just managing at the document level. That gave it a competitive edge over traditional document management tools like OpenText and Documentum. Over the last decade it has consolidated that position, and in content publishing its product is now something of a standard. Dow Jones, for example, is in the process of moving one of its major revenue streams, the Factiva financial information resource, from an Oracle-based system and an Autonomy document management system to MarkLogic. “They are doing a complete standardization on MarkLogic across all of their digital properties,” claims Bloom. The other mainstream area where MarkLogic has seen success over several years is in anti-terrorism. “That is a massively heterogeneous data use case. The US federal government has information sharing agreements with numerous other agencies in the US and elsewhere. But the US teams receiving the data don’t control the formats so they need to ensure they have a very flexible technology if they are going to be able to work with those. There are whole global anti-terrorism programs that are completely dependent on a MarkLogic database today,” he maintains. Those were the early adopters. But the number of companies who have realized they have a content management problem has swollen. “From auto manufacturers with service guides going back 30 years to public authorities managing real estate licenses, pretty much everyone has a content management problem,” he says. But in recent times the company has been aggressively pushing outside of that content-centric world: “One thing that has dramatically changed in MarkLogic’s business is the realization that not only is NoSQL technology great for all that unstructured data, it is also extremely powerful for heterogeneous data.” From banks to healthcare Two examples of that currently dominate his thinking. One involves a large international bank based in London which has come under regulatory pressure to bring its trading data together from 20 different systems so it can be analyzed in a ‘trade store.’ Those transactional systems — built in Sybase, Oracle, mainframe databases and others — were designed by the bank to operate independently, ironically for regulatory reasons. “The financial authority is now saying, ‘We’re going to regulate you as a single entity.’ That kind of heterogonous data problem is very difficult to solve with relational technology; you’d have to build a data model that describes all the different systems, all their different versions, and create a layer which transforms everything into a standard format. The issue is that whenever one of those source databases changes — adds a column, changes a table structure — you’d have to rewrite all those layers above it.” That is not necessary in a NoSQL environment, he says. “So it has turned out that not only are we really good for this unstructured data — rich media, video, and so on — we are also very good for traditional data too.” Another large-scale project where unstructured and structured data come together in huge quantities is HealthCare.gov, the ‘ObamaCare’ US Federal market for affordable health insurance, which uses MarkLogic as its underlying database system. While Bloom acknowledges there were serious problems at launch, the system was reconfigured last November and has been stable ever since, with high uptime and good response times. “We have all this unstructured policy data coming in from insurance companies and agencies across the US, none of which has a standard format. If they change their format and you were in a relational mode you’d have to change all your table structure to deal with those new formats that you don't control.” But MarkLogic also handles the transactional side of buying insurance, registering policies and passing them on to the insurance companies who manage the customer relationship. “We have now signed up several million US citizens to healthcare polices through the Healthcare.gov system, and the database is the hub for all the related IRS, tax data, immigration data, credit data.” The system is handling workloads of thousands of concurrent sessions, says: on a normal day 35,000, although peak periods have seen 80-85,000 users online. Relational staying power Bloom’s not saying companies should be thinking of abandoning their relational products — far from it. “It is just that there are some modern problems that relational is not so hot at. If you’re running your straight SAP general ledger on top of an Oracle dataset, I won’t recommend you use NoSQL for that. If you have purely unstructured then a NoSQL database is very good for that. If you are a cross between the two it depends on the application, but there is a huge class of applications that NoSQL is better for.” While relational rather than other NoSQL companies are MarkLogic’s primary competition today, Bloom says the NoSQL vendors — especially the majority whose products are based on open source NoSQL code — are actually the source if its best leads. Open lead generation That’s because MarkLogic is focused on serving corporate customers’ needs, he highlights. “How well do the open source NoSQL products service them? Some of them have backup, but that is about it. And they are going in with a story that there’s no security, there’s no high availability, there is no transactional consistency. What the open source guys have been for us is by far the best lead generation engine one could ever hope for.” “Open source companies like Cloudera and MongoDB have spend hundreds of millions of dollars evangalizing the market. Most of that money went on persuading people they need another database, and we’re a huge beneficiary of that,” he says. “They have helped people get familiar with NoSQL technology. They’re products are easy to download, run really fast, and allow you to build an application quickly. It is a great experience for developers who can solve a problem in a couple of hours that they may have been working on for six weeks with relational technology. But when they want to run that application in production, they find they don’t have [the enterprise fundamentals] expected in the Oracle, SQL Server, DB2 and Sybase world.” As Bloom emphasizes, we are just at the beginning of a market-reshaping trend. Companies are finding more and more challenges that are not well served by relational technologies. As someone who was once tipped as a possible successor to Oracle’s Larry Ellison, that profile as potential of giant slayer is very much to Bloom’s liking. “We are already the biggest in the market and the fact that the incumbents are so dismissive of what we are doing, well I love it. I’ve had a great career — a great time at Oracle, a really interesting time at Veritas — but this is certainly where I’ve had most fun.” Image: MarkLogic
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"https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/google.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/twitter.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/linkedin.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/facebook.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/google.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/twitter.png", "https://d15shllkswkct0.cloudfront.net/wp-content/plugins/wordpress-social-login/assets/img/32x32/wpzoom/linkedin.png", "https://pixel.quantserve.com/pixel/p-HgwXEEYzRpfFC.gif", "http://pixel.quantserve.com/pixel/p-0bRKhF16V5Cqk.gif" ]	[]	[]	[ "Paul Gillin", "SiliconANGLE", "MarkLogic claims to do data lakes one better with cloud integration engine" ]	null	[ "Paul Gillin", "DUNCAN RILEY", "DAVE VELLANTE", "MARIA DEUTSCHER", "ROBERT HOF", "JOHN FURRIER" ]	2018-10-09T00:00:00	MarkLogic claims to do data lakes one better with cloud integration engine - SiliconANGLE	en	https://d15shllkswkct0.c…g/favicon-SA.png	SiliconANGLE	https://siliconangle.com/2018/10/09/marklogic-claims-data-lakes-one-better-cloud-integration-engine/	MarkLogic Inc. is continuing to bulk up the data integration features it introduced last spring by combining its NoSQL database with its Data Hub and delivering the two as a service. The company claims the MarkLogic Data Hub Service is an alternative to data lakes that makes it simple to manage, curate, secure and use data without extensive preparation. Data Hub “is not an [extract/transform/load] process or a meta hub. The data actually comes in to MarkLogic, is stored as is and can be used immediately as it comes in,” said Joe Pasqua (pictured) the company’s executive vice president of products. “But then it is also curated and available for operational use within the hub.” Rather than transforming data, MarkLogic says it “harmonizes” it by leaving the original data in place. Data can be “wrapped” to create views that match users’ needs without disturbing the underlying data. “Rather than potentially throwing away the information, the source data is kept in place,” Pasqua said. MarkLogic uses Apache NiFi to import data, enabling it to handle a variety of datatypes such as unstructured documents, graphs, relational tables and geospatial data from sources such as relational engines, message buses and streaming data services. “Everything NiFi connects to we can connect to,” Pasqua said. Data from multiple sources can be integrated, governed, searched and queried within a single engine. The company’s Data Hub isn’t new, having been introduced in late 2016. What’s new with this announcement is the integration of the Data Hub with the underlying NoSQL engine and delivery as a service. “Now we run it for you,” Pasqua said. “You click a button, a whole Data Hub cluster is set up for you and you don’t have to configure anything.” MarkLogic has abstracted the underlying cloud resources out of the equation so that customers can pick a baseline capacity and pay a fixed amount with the assurance that surcharges won’t be applied. The service enables bursting to be applied to meet peak loads within a predictable cost. “When you use less than the baseline, you earn credits. When you use more, you use credits,” Pasqua said. Pricing for an entry-level Data Hub works out to about $4 per hour, he estimated. The company said the service provides enterprise-grade data security and reliability. “It’s always highly available, always encrypted,” Pasqua said. “There is no way not to get encryption or high availability.” Photo: SiliconANGLE
correct_foundationPlace_00033	FactBench	2	6	https://community.progress.com/s/products/marklogic	en	Progress Customer Community			[]	[]	[]	[ "" ]	null	[]	null		en	/s/sfsites/c/resource/FaviconSite?v=2		null
correct_foundationPlace_00033	FactBench	1	6	https://www.progress.com/marklogic/server/features/multi-model-database	en	Multi-Model NoSQL Database Features	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/marklogic-resources-hex-bg.svg?sfvrsn=b551503_5", "https://d117h1jjiq768j.cloudfront.net/images/default-source/resource-center/paper/whitepaper-3-thumbnail.svg?v=2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/resource-center/ebook/ebook-2-thumbnail.svg?v=2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/resource-center/video/video-1-thumbnail.svg?sfvrsn=57c920fc_9", "https://d117h1jjiq768j.cloudfront.net/images/default-source/marklogic/overview-ml/marklogic-overview-prefooter-bg.svg?sfvrsn=8a31df87_6&v=2" ]	[]	[]	[ "" ]	null	[]	null	Learn about what makes MarkLogic’s multi-model database the right solution for your enterprise.	en	/favicon.ico?v=2	Progress.com	https://www.progress.com/marklogic/server/features/multi-model-database	The Document Model – Flexible and Human-Oriented The document database model is the most flexible of the NoSQL data models, and the most popular. Documents are ideal for handling varied and complex hierarchical data. Humans can read them, they closely map to the conceptual or business model of the data, and they avoid the impedance mismatch problem that relational databases have. In summary, here are the main benefits of using the document database model: Fast development Schema-agnostic Data “denormalized” Leverages all attributes Queries everything in context Ideal for data integration To securely access and share documents, MarkLogic provides a built-in search engine, document and element level security controls, redaction policies, and more. The search engine automatically indexes documents for full-text search on ingestion and gives you the flexibility to define additional indexes (e.g., range indexes, geospatial indexes) and customize relevance ranking. This and various other out-of-box features (like facets, snippets, etc.) enable you to quickly build advanced search applications. Whether it’s Java objects that represent business entities or free-flowing text from a “document” in the more traditional sense (Microsoft Word documents, PDFs, etc.), they are all naturally stored as JSON and XML documents with strong consistency in the MarkLogic platform. Semantic Graph Database Model for Relationships Documents are fantastic for storing business entities, but when it comes to entity relationships, a semantic graph database model—another popular NoSQL model—is best. It’s designed to store and manage relationships among people, customers, providers, or any other entity of interest. Additionally, MarkLogic provides a semantic graph data model in the form of a built-in RDF triple store, which stores and manages semantic data. We call this capability MarkLogic Semantics. Semantics enhances the document model by providing a smart way to connect and enhance the JSON and XML documents. This facilitates data integration and enables more powerful querying to discover relationships and make inferences. Semantics also provides context for your data by storing metadata (e.g. ontologies). For example, consider a product catalog that has information about parts, and one part is listed with a size of “42”. But, where is the contextual information: What are the units of “42”? What is the tolerance? Who measured it? When was it measured? This contextual information is the semantics data, which can be stored as RDF triples in MarkLogic. Similar to the document model, the MarkLogic platform’s built-in search engine indexes RDF triples for fast execution of semantic searches using SPARQL queries. You can easily compose complex queries that combine semantic and document searches to discover insights. Geospatial Search Capabilities The document data model provides the flexibility to store geospatial data. MarkLogic can natively store, manage, and search geospatial data, including points of interest, intersecting paths, and regions of interest. This enables you to answer the “where” question in the context of all your other data (entities, relationships, etc.). The built-in search engine indexes geospatial data to power location-based search queries and alerts for geospatial applications. Learn more about how customers are using Geospatial to implement powerful location-based search applications. Structured, Relational Views of Data Relational data models are useful for a reason. Sometimes, it’s really convenient to have structured views of your data in a tabular form that you can query with good ol’ standard SQL. With MarkLogic, your developers will feel right at home. MarkLogic supports standard SQL. It allows you to create relational views on top of your data for SQL analytics without compromising data security. The underlying data never changes — it’s still available in its original format in the MarkLogic platform. The underlying technology that makes this level of SQL support possible is unique to MarkLogic . It’s called Template Driven Extraction (TDE). It enables you to define a relational lens over your data (or entities) so you can query it using standard SQL. Hence, you can use familiar BI tools for operational analytics. Integrated Indexes Multi-model databases provide a unified search interface to query multiple data models using integrated indexes. Typically, you have to choose and manage specific indexes for each data type. On the other hand, the MarkLogic platform has an integrated suite of indexes that allow fast data access – immediately after data is loaded. A multi-model database works more like Google — Google doesn’t require web pages to fit a certain format, it just indexes them and makes them accessible via a unified search interface. The MarkLogic platform’s built-in search engine indexes all data types and delivers exceptional search performance. Hence, users can quickly search data across multiple data models with a single, composable query. For example, you can combine semantic and search queries to find patients who are uninsured and suffer from chronic illness. Composable Queries Multi-model databases provide industry-standard query languages and APIs to flexibly store and access data for all the supported data models. With the MarkLogic platform, users can query data using Search, SQL, SPARQL, or REST API. It also supports multiple programming languages like JavaScript, Node, Java, and XQuery. As a true multi-model database, MarkLogic also provides its Optic API as a unified query interface for multi-model data access. It provides flexible and easy access to data across all data models. You can create single, composable query across documents, relational views, and semantic graphs (in any combination). For example, you can use the Optic API to search and filter documents, execute relational operations (like join or aggregate), and retrieve (or construct) documents on output. Try doing that with another multi-model database! Unified Platform A multi-model database complements its data modeling flexibility and unified query interface with a single data security, governance, and transactional model. As a unified data platform, it increases developers’ productivity and operational efficiency. As a true multi-model database, MarkLogic provides a unified data security, governance, and consistency model. It uses a shared-nothing architecture to provide scalability and availability, and reduces the operational footprint for development, testing, upgrades, backup and recovery, and more.
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correct_foundationPlace_00033	FactBench	1	13	https://www.dbta.com/BigDataQuarterly/Articles/MarkLogic-Plans-Next-Major-Release---QandA-with-MarkLogic-CEO-Gary-Bloom-112081.aspx	en	MarkLogic Plans Next Major Release - Q&A with MarkLogic CEO Gary Bloom	https://www.dbta.com/ima…al-logo-2019.png	https://www.dbta.com/ima…al-logo-2019.png	[ "https://www.dbta.com/images/icon_twitter.png", "https://www.dbta.com/images/icon_linkedin.png", "https://www.dbta.com/images/icon_facebook.png", "https://dzceab466r34n.cloudfront.net/BigDataQuarterly/post_icon_twitter.png", "https://dzceab466r34n.cloudfront.net/BigDataQuarterly/post_icon_linkedin.png", "https://dzceab466r34n.cloudfront.net/BigDataQuarterly/post_icon_facebook.png", "https://www.dbta.com/Images/Default.aspx?ImageID=19518&max=450&maxWidth=240&canvas=450x240", "http://dzceab466r34n.cloudfront.net/images_nl/DBTA/lp/Bloom.jpg", "https://www.dbta.com/Images/Default.aspx?ImageID=25058&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25089&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25089&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25086&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25085&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25070&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25068&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=25052&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24832&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24669&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24550&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=24468&maxWidth=125&canvas=125x165", "https://www.dbta.com/Images/Default.aspx?ImageID=23938&maxWidth=125&canvas=125x165", "https://www.dbta.com/images/logo_footer.png", "https://www.dbta.com/images/partnerlogos/idug-logo-sm-on-2.gif", "https://www.dbta.com/images/partnerlogos/iiug-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/isug-tech-7_sm.png", "https://www.dbta.com/images/partnerlogos/oaug-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/pass-logo-sm-on.gif", "https://www.dbta.com/images/partnerlogos/share_logo_small-new.png", "https://www.dbta.com/images/partnerlogos/ukoug-logo-sm-on.jpg", "https://www.dbta.com/images/partnerlogos/DBTA-Media-Partner-DSI-40.png" ]	[]	[]	[ "" ]	null	[ "Joyce Wells", "www.dbta.com" ]	2016-06-30T00:00:00	The next major release of MarkLogic's enterprise NoSQL database platform is expected to be generally available by the end of this year. Gary Bloom, president and CEO of the company, recently reflected on the changing database market and how new features in MarkLogic 9 address evolving requirements for data management in a big data world.	en		Database Trends and Applications	https://www.dbta.com/BigDataQuarterly/Articles/MarkLogic-Plans-Next-Major-Release---QandA-with-MarkLogic-CEO-Gary-Bloom-112081.aspx	New release will add capabilities for data integration, security, and manageability. The next major release of MarkLogic's enterprise NoSQL database platform is available for early access now and will be generally available by the end of this year. Gary Bloom, president and CEO of the company, recently reflected on the changing database market and how new features in MarkLogic 9 address evolving requirements for data management in a big data world. "For the first time in years, the industry is going through a generational shift of database technology - and it is a pretty material shift," observed Bloom. What are the challenges that customers are dealing with now as far as data management? Gary Bloom: If you look at what is going on in the marketplace, there is a massive problem that customers are struggling with and that has to do with data being in silos. What customers want is a 365-degree view of their data and they want it to be very actionable, meaning that they want to change their business processes, change their workflows, run their business differently based on the look of all of their data. However, the reality, partly driven by the architecture of the relational database model - and that has been the primary database model for the last 30 years – is that what winds up happening is that you get anything but a unified view. What you get is lots of different people taking snapshots of the data, and then doing different things with these snapshots. It is actually very hard to integrate those data silos. How does that get resolved? GB: The way that got solved in the relational database era was through ETL processes, where you essentially transformed everything and you put it into another relational database. The problem with the transformation process is that every time the source data changes, you have to rewrite your ETL processes and every time the user wants to ask a different question of the data you have to re-index the relational database to make the SQL statement run properly. What do you propose? GB: With MarkLogic’s operational and transactional enterprise NoSQL database we have come up with an approach in which ETL essentially goes away. MarkLogic ingests all the data as-is, including structured data and unstructured data, and it also includes all of information about the data –metadata. MarkLogic then creates a universal, ask-anything index over the data, and then from there, customers go ahead and build applications. We focus on the enterprise requirements as well: trusted transactions which the industry sometimes calls “ACID” capabilities, security, and high availability and disaster recovery. We check off all the boxes for what real data centers need to run their businesses. How do you do that? GB: Google does not change Google every time an organization publishes five new web pages. They just index that data in. What we have done is the exact same thing for corporate data, the big difference is that it is not just web pages, it is all your business data. Instead of taking mini snapshots of data – which, by the way creates an enormous cybersecurity risk because it results in copies of mission-critical business data in many repositories throughout an organization – we give customers the opportunity to create a unified layer where. All the data comes into the system with a unified view, and new workflows and applications can then be built on top of it. An example is Deutsche Bank which took 30-plus trading systems and, rather than do a bunch of ETL processes and put it back into a relational database, put a Mark Logic layer in. MarkLogic takes the data in from all the different trading systems and, once it is in MarkLogic, all the post-trade processing, including all regulatory compliance, is dealt with from a single integrated repository. Without the ETL processes, an application can be built in a fraction of the time. How does this help? GB: If you think about it, data scientists are spending about 80% of their time simply massaging and wrangling the data to get it into a format so they can actually do something with it. In a data warehouse about 60% of the expense is the cost of the ETL processes and buying that software and running the ETL processes so you can have a data warehouse. Essentially, what we do is we just let the customer integrate all that data into the MarkLogic enterprise NoSQL database platform. We essentially take out the cost dimension and the time dimension so our customers tend to build applications very rapidly. What is the biggest problem customers are dealing with as far as big data? GB: When all the companies started moving into this whole next-generation database market, many people thought it was all about the fact that there was all this unstructured data that could not be managed, or about the speeds and feeds of social media, networking, and Internet of Things data. Both of those are correct. Yes, there are the speeds and feeds problems and the data variety problems - the structured, unstructured, social media, video, voice data - but there is also the problem of integrating data in silos with data spread all over the enterprise, and that is predominantly structured data. One of our customers in the healthcare field had 140 HR systems, so if someone in the organization wanted to know something about the employees, data from 140 systems had to be brought together. It built a repository in MarkLogic and most of that data is structured data. The big data challenge is not just unstructured data. When we talk about integrating data in silos, it is all three of those data categories. It is structured data, the variety of data around unstructured, and the high volume data as well. Just like most of the modern architectures we run on pretty traditional scale-out, elastic Intel architecture. We became the database underneath Obamacare, bringing together all the healthcare policies so they could have an Amazon.com-like experience for people to purchase healthcare insurance in the U.S. MarkLogic 9 will be released later this year? How does it fit in? GB: It complements a lot of features that we have already built such as tiered storage, semantics - capabilities that allow organizations to have these big repositories and merge that data. What is new in the release? GB: In MarkLogic 9, we focused on three primary themes. Giving customers more tools to integrate data; that is number one. Then, if we are going to create this master repository of all trade data, or all HR data, all financial data, or all intelligence data, then security becomes really important, that is the number two. And third, is that these systems get pretty big, pretty fast and they running in complicated environments. For example, the system might include a physical on-premise data center in the U.S. and a cloud provider in Europe, so data must be moved between these different places. Those are the three things – continuing to improve the ease with which customers can integrate data once it is in the database; making it highly secure; and the third, giving the customer the tools to manage it. What has been done for data integration? GB: There are three primary pieces of the data integration strategy. One is continued evolution of our semantics capability. Version 9 brings support for conceptual relationships and semantic relationships, and it also brings in the ability to capture and query the model as you need it, which means that we are making it easier for the customer - once they have brought all the data together - to understand the relationship of the different elements of the data. The second piece to integration is the data query capability. We are dramatically improving our SQL API to allow more BI tools against a new generation of database as well as against the old generation database. It is really helping people cross the bridge to the next generation. And third is data movement. Organizations are bringing data in from existing databases, as well as batch processes, messaging streams, social media streams, all different kinds of data is coming in rapidly and this requires much more effective data movement capability and so what we are doing in MarkLogic 9 is just making it easier to do the ingestion process to get the data into MarkLogic. And security enhancements? GB: We already have Common Criteria certification which is a government certification driven by the fact that we do a lot of work in the government sector. In MarkLogic 9 we are adding two major features. One is advanced encryption. This is encryption for data at rest, but it is really transparent encryption meaning that we are encrypting the entire database so that someone who has access to the storage medium will not be able to see the data in the database. The second thing that is really major in the security world is redaction, which allows users to hide or mask any data in the database that they don’t want somebody to see. Even a DBA or a system administrator working with MarkLogic database can be restricted so that they can’t see the data in the database. In a healthcare application, this can protect the patients’ names, but still allow someone do analysis on all the information, or in a banking application to restrict data access to only a group of clients data not all the company’s clients. What does MarkLogic 9 add for manageability? GB: One of the things we are doing is introducing OpsDirector, a new user interface for the administrator over a MarkLogic environment to administer multiple clusters and multiple databases at scale. The second thing we are doing is adding rolling upgrades so we can support updating the database to new versions, putting new patches in, making changes to the database code itself without ever bringing down the production cluster. And the last thing we have added is a telemetry capability so that, if the customer opts in, MarkLogic can collect data directly from the customer. It dramatically improves the resolution of any issues and even more importantly it helps to proactively avoid and identify issues that could become problems. Why is driving these new capabilities? GB: Really, for the first time in years, the industry is going through a generational shift of database technology - and it is a pretty material shift. Almost all the analyst firms forecast that the database market will grow dramatically over the next 5 to 10 years, but virtually all the incumbent database suppliers have flattened out in their growth. What is happening is that these are not just new technologies being introduced but collectively they represent a generational shift in the database market. For MarkLogic, the reason security, availability, and data integration are so important is that we are integrating silos of data, but we are doing it in major corporations for mission critical computing. We are driving a generational shift in a part of the market that typically moves pretty slowly to new technologies because of the high standards that are there. Interview conducted, edited, and condensed by Joyce Wells.
correct_foundationPlace_00033	FactBench	2	11	https://github.com/marklogic	en	MarkLogic	https://avatars.githubusercontent.com/u/189902?s=280&v=4	https://avatars.githubusercontent.com/u/189902?s=280&v=4	[ "https://avatars.githubusercontent.com/u/189902?s=200&v=4" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic has 31 repositories available. Follow their code on GitHub.	en	https://github.com/fluidicon.png	GitHub	https://github.com/marklogic	People This organization has no public members. You must be a member to see who’s a part of this organization.
correct_foundationPlace_00033	FactBench	1	44	https://help.marklogic.com/Knowledgebase/Article/View/marklogic-search-faq	en	MarkLogic Search FAQ	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://help.marklogic.com/__swift/themes/client/images/ml-loader.gif", "https://help.marklogic.com/__swift/themes/client/images/icon_star_5.gif" ]	[]	[]	[ "" ]	null	[]	null				Progress.com	https://www.progress.com/resources	Question Answer Further Reading What is MarkLogic's Built-In search feature? MarkLogic is a database with a built-in search engine, providing a single platform to load data from different silos and search/query across all of that data It uses an "Ask Anything" Universal Index where data is indexed as soon as it is loaded - so you can immediately begin asking questions of your data You want built-in search in your database because it: Removes the need for a bolt-on search engine for full-text searches, unlike other databases Enables you to immediately search/discover any new data loaded into MarkLogic, while also keeping track of your data as you harmonize it Can be leveraged when building apps (both transactional and analytical) that require powerful queries to be run efficiently, as well as when you want to build Google-like search features into your application Documentation: Built-in search Search All Your Data With an "Ask Anything" Universal Index "Ask Anything" Universal Index What features are available with MarkLogic search? MarkLogic includes rich full-text search features. All of the search features are implemented as extension functions available in XQuery, and most of them are also available through the REST and Java interfaces. This section provides a brief overview some of the main search features in MarkLogic and includes the following parts: High Performance Full Text Search Search APIs Support for Multiple Query Styles Full XPath Search Support in XQuery Lexicon and Range Index-Based APIs Alerting API and Built-Ins Semantic Searches Template Driven Extraction (TDE) Where to Find Additional Search Information Documentation: Searching in MarkLogic Server Developing Search Applications in MarkLogic Server Search Customization using Query Options KB Article: Semantics, SQL, TDE, and Optic Primer What are the various search APIs provided by MarkLogic? MarkLogic provides search features through a set of layered APIs. The built-in, core, full-text search foundations are the XQuery cts:* and JavaScript cts.* APIs The XQuery search:, JavaScript jsearch., and REST APIs above this foundation provide a higher level of abstraction that enable rapid development of search applications. E.g.: The XQuery search:* API is built using cts:* features such as cts:search, cts:word-query, and cts:element-value-query. On top of the REST API are the Java and Node.js Client APIs that enable users familiar with those interfaces access to the MarkLogic search features This diagram illustrates the layering of the Java, Node.js, REST, XQuery (search and cts), and JavaScript APIs. Documentation: Search APIs What happens if you decide to change your index settings after loading content? The index settings are designed to apply to an entire database and MarkLogic Server indexes records (or documents/fragments) on ingestion based on these settings. If you change any index settings on a database in which documents are already loaded: If the “reindexer” setting on the database is enabled, reindexing happens automatically Otherwise, one should force reindex through the “reindex” option on the database “configure” page or by reloading the data Since the reindexer operation is resource intensive, on a production cluster, consider scheduling the reindex during a time when your cluster is less busy. Additionally, as reindexing is resource intensive, you’ll be best served to test any index changes on subsets of your data (as reindexing subsets will be faster and use fewer resources), then only promote those index changes to your full dataset once you’re sure those index settings are the ones you’ll want going forward Documentation: Text indexes KB Article: Indexing best practices What is the role of language baseline setting? What are the differences between legacy and ML9 settings? The language baseline configuration is for tokenization and stemming language support. The legacy language baseline setting is specified to allow MarkLogic to continue to use the older (MarkLogic 8 and prior versions) stemming and tokenization language support, whereas the ML9 setting would specify that the newer MarkLogic libraries (introduced in MarkLogic 9) are used. If you upgrade to MarkLogic 9 or later from an earlier version of MarkLogic, your installation will continue to use the legacy stemming and tokenization libraries as the language baseline. Any fresh installation of MarkLogic will use the new libraries. If necessary, you can change the baseline configuration using admin:cluster-set-language-baseline. Note: In most cases, stemming and tokenization will be more precise in MarkLogic 9 and later. Documentation: Known incompatibilities with previous releases What is the difference between unfiltered vs filtered searches? In a typical search: MarkLogic Server will first do index resolution from the D-Nodes - which results in unfiltered search results. Note that unfiltered index resolution is fast but may include false-positive results As a second step, the Server will then do filtering of those unfiltered search results on the E-Nodes to remove false positives from the above result set - which results in filtered search results. In contrast to unfiltered searches, filtered searches are slower but more accurate While searches are filtered by default, it is often also possible to explicitly perform a search unfiltered. In general, if search speed, scale, and accuracy are priorities for your application, you’ll want to pay attention to your schemas and data models so unfiltered searches return accurate results without the need for the slower filtering step Documentation: cts:search Fast Pagination and Unfiltered Searches KB Articles: Fast searches: resolving from the indexes vs. filtering When should I look into query or data model tuning? Is filtering during a search bad? Filtering isn’t necessarily bad but: It is still an extra step of processing and therefore not performant at scale A bad data model often makes things even worse because they’ll typically require unnecessary retrieval of large amounts of unneeded information during index resolution - all of which then will be filtered on the e-nodes To avoid performance issues with respect to filtering, try: Adding additional indexes Improving your data model to more easily index/search without filtering Structuring documents and configuring indexes to maximize both query accuracy and speed through unfiltered index resolution alone Documentation: cts:search Fast Pagination and Unfiltered Searches KB Articles: Fast searches: resolving from the indexes vs. filtering When should I look into query or data model tuning? What is the difference between cts.search vs jsearch? cts.search() runs filtered by default. JSearch runs unfiltered by default. JSearch can enable filtering by chaining the filter() method when building the query: http://docs.marklogic.com/DocumentsSearch.filter Note: Filtering is not performant at scale, so the better approach is to tune your data model and indexes such that filtering is not necessary. Documentation: cts:search Creating JavaScript search applications How do data models affect Search? Some data model designs pull lots of unnecessary data from the indexes with every query. That means your application will: Need to do a lot of filtering on the e-nodes Use more CPU cycles on the e-node to do that filtering Even with filtering disabled, you’re still be pulling lots of position information from the indexes - which means you’ll be using lots of CPU on the e-nodes to evaluate which positions are correct (and unlike filtering, position processing can’t be toggled on/off) Retrieving more data means an increased likelihood of CACHEFULL errors How you represent your data heavily informs the speed, accuracy, and ease of construction of your queries. If your application needs to perform and/or scale, its data model is the first and most important thing on which to focus Documentation: Data Modeling Tutorial KB Articles: When should i look into query or data-model tuning? Performance Issues in MarkLogic Server: what they look like - and what you should do about them How do I optimize my application’s queries? There are several things to consider when looking at query performance: How fast does performance need to be for your application? What indexes are defined for the database? Is your code written in the most efficient way possible? Can range indexes and lexicons speed up your queries? Are your server parameters set appropriately for your system? Is your system sufficiently large for your needs? Access patterns and resource requirements differ for analytic workloads Here is a checklist for optimizing query performance: Is your query running in “Accidental” update mode? Are you running cts:search unfiltered? Profile your code Use indexes when appropriate Optimize cts:search using indexes Tuning queries with query-meters and query-trace Documentation: Query Performance and Tuning Guide Tuning Queries with query-meters and query-trace Blog: A checklist for optimizing Query Performance KB Article: Performance Issues in MarkLogic Server: what they look like - and what you should do about them How to ensure wildcard searches are fast? The following database settings can affect the performance and accuracy of wildcard searches: word lexicons element, element attribute, and field word lexicons. (Use an element word lexicon for a JSON property). three character searches, two character searches, or one character searches. You do not need one or two character searches if three character searches is enabled. three character word positions trailing wildcard searches, trailing wildcard word positions, fast element trailing wildcard searches fast element character searches The three character searches index combined with the word lexicon provides the best performance for most queries, and the fast element character searches index is useful when you submit element queries. One and two character searches indexes are only used if you submit wildcard searches that try to match only one or two characters and you do not have the combination of a word lexicon and the three character searches index. Because one and two character searches generally return a large number of matches and result in much larger index storage footprints, they usually are not worth subsequent disk space and load time trade-offs for most applications Lastly, consider using query plans to help optimize your queries. You can learn more about query optimization by consulting our Query Performance and Tuning Guide Documentation: Recommended Wildcard Index Settings Understanding the Wildcard Indexes Understanding and using Wildcard Searches Blog: The Secrets to Wildcard Search in MarkLogic What are the factors that affect relevance score calculations? The score is a number that is calculated based on Statistical information, including the number of documents in a database The frequency in which the search terms appear in the database The frequency in which the search term appears in the document The relevance of a returned search item is determined based on its score compared with other scores in the result set, where items with higher scores are deemed to be more relevant to the search. By default, search results are returned in relevance order, so changing the scores can change the order in which search results are returned. Documentation: Understanding How Scores and Relevance are Calculated KB Article: Understanding Term Frequency rules for relevance calculations How do I restrict my searches to only parts of my documents (or exclude parts of my documents from searches altogether)? MarkLogic Server has multiple ways to include/exclude parts of documents from searches. At the highest level you can apply these restrictions globally by including/excluding elements in word queries. Alternative (and preferably), you can also define specific fields, which are a mechanism designed to restrict searches to specifically targeted elements within your document KB Article: Best practices when trying to search (or not search) parts of documents How do I specify that the match must be restricted to the top level attributes of my JSON document? You can configure fields in the database settings that are used with the cts:field-word-query, cts:field-words, and cts:field-word-match APIs, as well as with the field lexicon APIs in order to fetch the desired results. You can create a field for each top-level JSON property you want to match with indexes. In the field specification you should use a path expression /property-name for the top-level property "property-name". Then use field queries to match the top level property. Depending on your use-case, this could be an expensive operation due to the indexes involved resulting in slower document loads and larger database files. Documentation: Fields Database Settings
correct_foundationPlace_00033	FactBench	2	85	https://www.govloop.com/community/blog/marklogic-6-an-introduction/	en	MarkLogic 6: An introduction » Community	http://ctovision.com/wp-content/uploads/marklogic.png	http://ctovision.com/wp-content/uploads/marklogic.png	[ "https://www.facebook.com/tr?id=732801913406742&ev=PixelInitialized", "https://www.govloop.com/wp-content/themes/govloop-theme/library/images/govlooplogo.png", "https://secure.gravatar.com/avatar/d3418aa0207ed63236b3f4a3d08314ca?s=96&d=mm&r=g", "http://ctovision.com/wp-content/uploads/marklogic.png", "http://www.marklogic.com/images/2012/10/ml-icon-bitools.gif", "http://www.marklogic.com/images/2012/10/ml-icon-rest.gif", "http://www.marklogic.com/images/2012/10/ml-icon-java.gif", "http://www.marklogic.com/images/2012/10/ml-icon-java.gif", "http://www.marklogic.com/images/2012/10/ml-icon-widgets.gif", "http://www.marklogic.com/images/2012/10/ml-icon-functions.gif", "http://www.marklogic.com/images/2012/10/ml-icon-functions.gif", "http://www.marklogic.com/images/2012/10/ml-icon-pump.gif", "http://www.marklogic.com/images/2012/10/ml-icon-security.gif", "http://www.marklogic.com/images/2012/10/ml-icon-search.gif", "http://www.marklogic.com/images/2012/10/ml-icon-index.gif", "http://www.marklogic.com/images/2012/10/ml-icon-search.gif", "http://img.zemanta.com/pixy.gif?x-id=dedfef05-620c-4aa1-9645-81d9b746723f", "http://feeds.feedburner.com/~r/typepad/ctovision/cto_vision/~4/-10ETM93M6A", "https://www.govloop.com/wp-content/themes/govloop-theme/library/images/govloop-gray-logo.svg" ]	[]	[]	[ "" ]	null	[ "Ryan Kamauff" ]	2013-02-13T23:59:27+00:00	By Ryan Kamauff The latest permeation of MarkLogic (version 6) offers ACID (atomicity, consistency, isolation, durability) transactions, horizontal scaling, real-time indexing, high availability, disaster recovery, government-grade security and built-in search. MarkLogic has made application development easier with Java and REST APIs. They also added JSON support. This allows developers to use their language of choice,Read... Read more »	en	https://www.govloop.com/…e-icon-touch.png	GovLoop	https://www.govloop.com/community/blog/marklogic-6-an-introduction/	MarkLogic 6: An introduction By Ryan Kamauff The latest permeation of MarkLogic (version 6) offers ACID (atomicity, consistency, isolation, durability) transactions, horizontal scaling, real-time indexing, high availability, disaster recovery, government-grade security and built-in search. MarkLogic has made application development easier with Java and REST APIs. They also added JSON support. This allows developers to use their language of choice, and eliminate the need to learn new production language. It also provides data visualization widgets. These widgets can display the shape and dimensions of data, identify trends or patterns and explore the data as a whole. Version 6 also comes with built in database analytics. Integration with IBM Cognos and Tableau is included as well. This enables analysts to create reports, dashboards and the data. Lastly, version 6 adds the in-database MapReduce capabilities. Find out more about MarkLogic version 6 here. And here is more from their website: Mission-critical Big Data Applications around the world are powered by MarkLogic. It is the only Enterprise NoSQL database that manages all types of data at scale in real time. It gives you the range of features you need to deliver value. It lets you leverage your existing tools, knowledge, and experience. And it provides a reliable, scalable, and secure platform for your important data. New Feature Highlights Business Intelligence Tools Big Data in the enterprise needs to be accessible to everyone who could benefit from the information. To make that easier, MarkLogic now includes out-of-the-box integration with Business Intelligence tools like IBM Cognos and Tableau to allow analysts to use familiar solutions for generating reports, dashboards and data exploration results from data stored in MarkLogic. REST API In order to enable developers to work in their language of choice, MarkLogic now includes a REST API that allows you to perform searches, create documents, read documents, update documents, and delete documents. The REST API allows you to build fully functional MarkLogic applications in any programming language. It also allows you to directly load JSON documents. MarkLogic Java API The new Java API allows you full-featured access to MarkLogic functionality with pure Java. The MarkLogic Java API is written on top of the REST API, and has all of its functionality such as paginated search with facets and snippets, full document CRUD operations, and more. Enhanced JSON Support Our new JSON library makes it easy to store JSON documents as key-value stores, and to convert them back and forth between JSON and XML. The REST Client API and the MarkLogic Client API for Java make use of this functionality to make it easier to load and work with JSON documents. Visualization Widgets We’ve also added Visualization Widgets so you can easily build powerful applications that help your users discover the shape and dimensions of data, quickly assess trends and patterns, and explore data more intuitively. You can access these widgets with MarkLogic Application Builder. In-database MapReduce & User Defined Functions Many customers wanted more flexibility to develop complex, real-time analytics. We’ve extended MarkLogic 6 to let you create user-defined aggregate functions (UDFs) that take advantage of MarkLogic’s parallel-processing architecture. We call this In-database MapReduce, and it lets you create blindingly fast analytic functions with custom C++ code by writing “map” and “reduce” functions. In-Database Analytic Functions In order to enable customers to leverage the power of the MarkLogic platform to produce enterprise-grade analytics, we’ve included several built-in XQuery functions to perform analytic and statistical functions. MarkLogic Content Pump (mlcp) In order to speed loading and exporting of data between databases, we are introducing the MarkLogic Content Pump (mlcp). mlcp is a command-line tool for loading content into MarkLogic Server and for migrating content from one instance of MarkLogic to another, even if they are on different platforms. If you have a Hadoop cluster, mlcp takes advantage of Hadoop to parallelize the loading. mlcp takes much of the functionality of the open source projects Record Loader and xqsync and bundles them in a single package, and allows them to take advantage of Hadoop if it is available; Hadoop is not required to use mlcp, but is used if it is available. FIPS 140-2 Cryptographic Compliance MarkLogic 6 includes the OpenSSL Federal Information Processing Standard (FIPS) Object Module, which was evaluated by the National Institute of Standards (NIST) for FIPS 140-2 compliance. More details on the OpenSSL Object Module and on the FIPS 140-2 compliance. Search API Enhancements When you’ve got a lot of data, great search is key. We’ve worked to make our search API even better with the following enhancements: Structured Search Extracting metadata at search time Modify the unconstrained term behavior using <search:term> Range constraints for path range indexes Ability to return values from range indexes with search:values JSON key support Path Range Indexes In order to enable fine-grained range indexes while maintaining the advantages of using the lexicon functions and the range query constructors MarkLogic now includes support for range index specified by a path. You can specify a subset of XPath as the definition of what goes into an index. The search API can take advantage of path range indexes to create range constraints on them. Path range indexes are also useful when setting up SQL views on data stored in a MarkLogic database. Synonym Search You now have the option to ensure that documents that contain multiple synonyms are scored appropriately, rather than unnaturally gaining points to the search score. Learn more about how the architecture of MarkLogic works and how you can deploy it. Original post
correct_foundationPlace_00033	FactBench	1	87	https://www.zdnet.com/article/marklogics-ceo-on-healthcare-gov-and-dueling-with-oracle/	en	MarkLogic's CEO on Healthcare.gov and dueling with Oracle	https://www.zdnet.com/a/…t=675&width=1200	https://www.zdnet.com/a/…t=675&width=1200	[ "https://www.zdnet.com/a/img/resize/f1474204f4afdd9e5d3e8a66676dd4bdae30d4c0/2022/08/05/bca73539-dbd2-46c5-8de4-f286b3d3e73b/stephanie-condon.jpg?auto=webp&fit=crop&frame=1&height=192&width=192", "https://www.zdnet.com/a/img/resize/8962d55dfe78d266d02d4803fba312fc5d837858/2016/05/17/122529de-1f16-4198-aa7d-34cc7a3f0932/garybloom-720.jpg?auto=webp&width=1280" ]	[]	[]	[ "" ]	null	[ "Stephanie Condon" ]	2016-09-26T10:00:00+00:00	As Healthcare.gov ramps up for the next open enrollment period, MarkLogic's CEO Gary Bloom talks about the shifting database needs.	en	https://www.zdnet.com/a/…-logo-yellow.png	ZDNET	https://www.zdnet.com/article/marklogics-ceo-on-healthcare-gov-and-dueling-with-oracle/	Open enrollment on HealthCare.gov, the federal database through which US citizens can sign up health care, begins for the fourth year on November 1. The website's launch was infamously botched in 2013, and the government recovered by moving part of the massive endeavor from Oracle to MarkLogic, which offers a non-relational, document-based (NoSQL) databases. MarkLogic's CEO Gary Bloom says it's not uncommon for his company's customers to come to them with a project originally started on an Oracle database. Still, the drama around health care databases continues: Just a few weeks ago, the state of Oregon announced it was settling its years-long legal battle with Oracle over its health care marketplace. When Oregon gave up on its state-run database back in 2014, it turned to the federal marketplace. Bloom's career has intersected with Oracle at multiple points. Bloom was an executive president at Oracle from 1986 to 2000. He was also on the board of Taleo, which was later acquired by Oracle. Before coming to MarkLogic, Bloom was CEO of Veritas, acquired by Symantec, and eMeter, acquired by Siemens. ZDNet caught up with Bloom ahead of open enrollment -- and after Oracle CTO Larry Ellison's fiery takedown of Amazon at Oracle OpenWorld -- to discuss the future of databases. This conversation was edited for brevity: Is HealthCare.gov a unique case, or does offer insight into bigger market challenges? I don't think what MarkLogic accomplished with Obamacare is any different from what enterprises around the globe are facing right now. They need to build their databases to integrate their silos of data. For Oregon, the only system Oracle was trying to build was the database. When we talk about integrating all the sources of data-- things like IRS, immigration and credit data on citizens -- that was all part of the Data Services Hub built by MarkLogic and run by the Department of Health Services. The Department of Health Services ran that as a service for all the state services. In a funny way, Oregon had a much smaller challenge -- which they could not complete -- than the feds had. For the federal market, we had to build the federal marketplace, as well as the Data Services Hub. If I look at that Data Services Hub, it's an extremely common problem that says, 'I have lots of data coming from lots of places, and I need a united, 360-degree look at that data.'.. In the health market more broadly, how do you get a 360-degree look at the patient? That includes the hospital systems, the insurance system, the pharmacy, the bill payment systems, the lab systems... It's a very common problem and probably one of the most difficult technology problems customers are facing today -- the need to integrate data from silos created over the last 50 or 60 years, both mainframe and relational-created silos. Oregon agreed to settle with Oracle for $25 million in cash and six years of Oracle software... When you look at Oregon, it's natural to try to stick with historically dominant vendors. The general view is that's the path with least resistance, with the least amount of risk. It often turns out that's the most expensive approach, and it's certainly the path of least value. When I look at Oregon and I look at the settlement, they spent several years trying to deliver the system, they hired Oracle to do that work and had to throw the towel into the ring and sign up with the federal system. [This] makes it a strange settlement. Essentially, the reason Oregon failed with the health care system is that they used their comfortable, incumbent technology. Now they're saying as part of the settlement, they can modernize the state government's IT system. Well, you're trying to modernize with yesterday's technologies... They're kind of doubling down on the incumbent technology that just failed them. I'm puzzled as to how the citizens of Oregon are going to benefit from this. Having almost unlimited use of the technology for six years seems a little bit strange given the massive scale of the failure of the health care system. Why is this so challenging for other non-relational databases? I don't think it's necessarily a technical challenge -- it's a challenge of the design point of your product. The design point for MarkLogic was always enterprise class customers. If I'm moving to the new generation from the prior generation, I don't leave behind my enterprise requirements -- that's availability, performance, security -- all the things a typical data center requires if you're going to run your business on the product. The vast majority of the products in the NoSQL environment were not designed for enterprise class customers, they were designed for the web developer. If you actually go back in history, when Oracle first came out they didn't worry about those data center features, either... Oracle added a lot of those enterprise capabilities as an afterthought. We actually built all that capability into MarkLogic. If you look at something like the federal marketplace for health care, this past year we ran upwards of 280,000 concurrent users, we ran about 5,500 transactions a second, so massive workloads. And then, oh by the way, we have IRS data, immigration data, credit data, personal and private information on residents of the United States -- so security obviously is a requirement. So we've always focused on that... In the current version we're moving forward on [MarkLogic 9], we're going to take our NoSQL database and make it the most secured database in the world -- not just the most secured NoSQL database. You've said that about half your customers come with projects in hand that started with Oracle. How do they end up working with MarkLogic? There's been a dominant database in the marketplace for now almost 30 years, being Oracle and the relational database model. What customers do is go to the technology they know... Real thought leaders think about next generation technologies, in a funny way, to solve a problem that relational technology was never designed to solve, and that's the issue of integrating data from lots of different sources with lots of different structures. What we've seen in our customer base is most customers start out with the incumbent product, whether that be DB2 or Microsoft Single Server or Oracle -- Oracle more frequently than not because of market share. Then after literally sometimes years of frustration, they say there's a different way to solve this problem. They're getting frustrated trying to integrate silos of data to get a 360-degree view of their customers or to integrate data for something like an operational or transactional system... Relational technology was never designed to solve that problem, so people move on. Why not move to an Oracle NoSQL product? Oracle does have a product they label NoSQL technology -- it's very different than ours. We offer a document database, they offer a key value database, which is the Berkeley DB acquisition they did. What happens is they push their incumbent product where they have dominant market share. They tend to not push their NoSQL product -- it really isn't competitive in the marketplace. If they convince a customer that relational isn't the product to solve it, customers will move on to other technologies other than Oracle's product... It's kind of a sideshow for Oracle, and for us it's our primary business. Larry Ellison slammed Amazon on several points at Oracle OpenWorld, one of them being vendor lock-in. What did you make of his attacks? When you talk about vendor lock-in, we're no different than Oracle -- we have customers running on Amazon, on Azure, on the Google Compute Platform, and obviously we have a lot of customers on their private cloud or on premise. I think where the Oracle attack was a little bit misguided is its [focus on] platform-as-a-service You're going there to get compute capacity, and it actually is relatively easy for customers to move from one compute platform to another. It's very hard if you have to start rewriting all your applications. So the vendor lock-in actually happens at the application layer... When I think about the focus on Amazon, I think about it more like about what happened 30 years ago with IBM. When Oracle started moving into the database market, IBM said we're going to provide end-to-end services, which led to the creation of IBM Global Services and kind of a redefinition of essentially what IBM was... They decided, 'We're going to be a leader in the services business.' That distracted everybody from what was happening to IBM's core business 30 years ago. New technologies and new platforms were starting to erode IBM's mainframe dominance. The exact same thing is happening today. What Oracle's starting to do now... they're saying we're a cloud provider -- they're creating a very similar distraction layer, saying, 'I'm going to take Wall Street's focus and move it from the database to the cloud and say, 'Measure me on nothing but the cloud.' In reality, Oracle's core business is starting to be eroded by next generation technologies. Larry's focus, Mark Hurd's focus, it's really an applications level argument, it's not necessarily tied to what's going on in their database business. One interesting thing we heard about at OpenWorld is Oracle's turn to AI. What tools would you say are needed to meet today's big data challenges? At the foundation of artificial intelligence, machine learning and all these new tricks that everyone wants to apply against their data, at the heart of it is, I have to bring the data together. If I look over at Watson from IBM, if the data can't get into Watson and I can't load my data, Watson can't be very intelligent about the data. As Oracle starts focusing on AI technology, one of the requirements is I have to be able to integrate my data into a repository under which something smarter in the logic can start to figure out the meaning. This is exactly at the heart of the difference between Oracle and MarkLogic. Oracle essentially created data silos because of the rigidity of their technology -- every time you wanted to do something with the data, you would create a new copy of the data for the different application. We take all those data silos and integrate it together into a common repository, and AI becomes just one of the asks, no different than some people wanting to run transactions. Some people want to do real time analytics, some want to do search and discovery -- so the idea is you need a flexible data platform where you can do all these different workloads from a common repository, exactly opposite of what happens in a relational database. In a relational database you keep making more copies of the data. Still today it suffers from its inability to process effectively unstructured data. At the end of the day, you're only working with a subset of your data that fits nicely into Oracle row and column infrastructure.
correct_foundationPlace_00033	FactBench	1	68	https://www.mytectra.com/interview-question/marklogic-interview-questions-and-answers	en	MarkLogic Interview Questions and Answers	https://www.mytectra.com…0DAFFcGkaHQo.png	https://www.mytectra.com…0DAFFcGkaHQo.png	[ "https://www.mytectra.com/hs-fs/hubfs/mytectra-logo/mytectra-logo-navi.png?width=150&height=50&name=mytectra-logo-navi.png", "https://no-cache.hubspot.com/cta/default/6445933/88645e8e-bdb2-40a0-9584-1db16d84b435.png", "https://no-cache.hubspot.com/cta/default/6445933/8780f248-e134-4931-b93a-d0b317eb68da.png", "https://no-cache.hubspot.com/cta/default/6445933/1c5e618b-c622-43c4-b8a8-489f7be416c5.png", "https://www.mytectra.com/hs-fs/hubfs/mytectra-logo/mytectra-logo-navi.png?width=150&height=50&name=mytectra-logo-navi.png", "https://no-cache.hubspot.com/cta/default/6445933/88645e8e-bdb2-40a0-9584-1db16d84b435.png", "https://no-cache.hubspot.com/cta/default/6445933/8780f248-e134-4931-b93a-d0b317eb68da.png", "https://no-cache.hubspot.com/cta/default/6445933/1c5e618b-c622-43c4-b8a8-489f7be416c5.png", "https://no-cache.hubspot.com/cta/default/6445933/0b866e6e-ad9f-43cd-9f14-6757333d941b.png", "https://no-cache.hubspot.com/cta/default/6445933/8780f248-e134-4931-b93a-d0b317eb68da.png", "https://www.mytectra.com/hs-fs/hubfs/website/images/interview-questions/Canva%20Design%20DAFFcGkaHQo.png?width=2240&name=Canva%20Design%20DAFFcGkaHQo.png", "https://www.mytectra.com/hs-fs/hubfs/website/images/cta/Marklogic%20banner.png?width=1106&name=Marklogic%20banner.png", "https://www.mytectra.com/hubfs/beautiful-beauty-brazilian-woman-1102343.jpg", "https://www.mytectra.com/hubfs/website/images/aathirai/aathirai-cut-mango-ads.png" ]	[]	[]	[ "" ]	null	[ "Sachin" ]	2022-07-04T15:10:06+00:00	Real-time MarkLogic interview questions with answers by experts will help you to crack your next MarkLogic job interview	en	https://www.mytectra.com/hubfs/favicon.ico		https://www.mytectra.com/interview-question/marklogic-interview-questions-and-answers	Q1. What is the need of NoSql databases? Ans When compared to relational databases, NoSQL databases are often more scalable and provide superior performance. In addition, the flexibility and ease of use of their data models can speed development in comparison to the relational model, especially in the cloud computing environment. Q2. Why MarkLogic? Ans MarkLogic is not only a NoSQL database, it's the only enterprise NoSQL database. This means that it comes with all of the features that traditional databases have—features that enterprises need. MarkLogic Server is designed to securely store and manage a variety of data to run transactional, operational, and analytical applications. Q3. What is cluster in MarkLogic? Ans The cluster has multiple machines (hosts), each running an instance of MarkLogic Server. Each host in a cluster is sometimes called a node, and each node in the cluster has its own copy of all of the configuration information for the entire cluster. Q4. What is FLWOR expression? Ans FLWOR (pronounced "flower") is an acronym for "For, Let, Where, Order by, Return". For - selects a sequence of nodes Let - binds a sequence to a variable Where - filters the nodes Order by - sorts the nodes Return - what to return (gets evaluated once for every node) Q5. What are the indexes in MarkLogic? Ans Word Indexing. Phrase Indexing. Relationship Indexing. Value Indexing. Word and Phrase Indexing. Q6. How is data stored in MarkLogic? Ans It uses XML and JSON documents as its data model, and stores the documents within a transactional repository. It indexes the words and values from each of the loaded documents, as well as the document structure. Q7. What data formats can be loaded into MarkLogic? Ans MarkLogic supports the following document formats: Q8. How do I create a MarkLogic database? Ans Q9. What is triple in MarkLogic? Ans Each document can contain multiple triples. The setting for the number of triples stored in documents is defined by MarkLogic Server and is not a user configuration. Ingested triples are indexed with the triples index to provide access and the ability to query the triples with SPARQL, XQuery, or a combination of both. Q10. How can you query triples in MarkLogic? Ans You can use the following methods to query triples: SPARQL mode in Query Console. XQuery using the semantics functions, and Search API, or a combination of XQuery and SPARQL. HTTP via a SPARQL endpoint. Q11. How can you query RDF dataset in MarkLogic? Ans You can query an RDF dataset using any of these SPARQL query forms: SELECT Queries - A SPARQL SELECT query returns a solution, which is a set of bindings of variables and values. CONSTRUCT Queries - A SPARQL CONSTRUCT query returns triples as a sequence of sem:triple values in an RDF graph. These triples are constructed by substituting variables in a set of triple templates to create new triples from existing triples. DESCRIBE Queries - A SPARQL DESCRIBE query returns a sequence of sem:triple values as an RDF graph that describes the resources found. ASK Queries - A SPARQL ASK query returns a boolean (true or false) indicating whether a query pattern matches the dataset. Q12. How to execute a SPARQL Query in Query Console? Ans To execute a SPARQL query: In a Web browser, navigate to the Query Console: http://hostname:8000/qconsole where hostname is the name of your MarkLogic Server host. From the Query Type drop-down list, select SPARQL Query. The Query Console supports syntax highlighting for SPARQL keywords. Construct your SPARQL query. See Constructing a SPARQL Query. You can add comments prefaced with the hash symbol (#). From the Content Source drop-down list, select the target database. In the control bar below the query window, click Run. Q13. What is MarkLogic data hub? Ans The MarkLogic Data Hub isan open-source software interface that works to ingest data from multiple sources, harmonize that data, master it, and then search and analyze it. It runs on MarkLogic Server, and together, they provide a unified platform for mission-critical use cases. Q14. Are binaries searchable in MarkLogic? Ans External binaries require special handling at load time because they are not managed by MarkLogic. Q15. What is needed in order to communicate with a particular database in MarkLogic via the REST API? Ans Getting Started with the MarkLogic REST API Q16. Which of the following features are available with MarkLogic search? Ans High Performance Full Text Search Search APIs Support for Multiple Query Styles Full XPath Search Support in XQuery Lexicon and Range Index-Based APIs Alerting API and Built-Ins Semantic Searches Template Driven Extraction (TDE) Where to Find Additional Search Information Q17. What type of query requires the triple index in MarkLogic? Ans Searches with the cts:triple-range-query constructor require the triple index; if the triple index is not configured, then an exception is thrown. The subjects to look up. When multiple values are specified, the query matches if any value matches. Q18. Which of the following layers are included in MarkLogic architecture? Ans The MarkLogic Reference Application Architecture is a three-tier model containing database, middle, and browser tiers. Q19. Which of these provide a way to load data into a MarkLogic database? Ans
correct_foundationPlace_00033	FactBench	2	93	https://medium.com/%40anshumankaku/connecting-marklogic-with-python-94915ed30edc	en	Connecting MarkLogic With Python	https://miro.medium.com/…i2_Tn3v42dw.jpeg	https://miro.medium.com/…i2_Tn3v42dw.jpeg	[ "https://miro.medium.com/v2/resize:fill:64:64/1dmbNkD5D-u45r44go_cf0g.png", "https://miro.medium.com/v2/resize:fill:88:88/1M_HGhkZsY-FJsV4JFWrY6Q.png", "https://miro.medium.com/v2/resize:fill:144:144/1*M_HGhkZsY-FJsV4JFWrY6Q.png" ]	[]	[]	[ "" ]	null	[ "Anshuman Srivastava", "medium.com" ]	2020-05-28T11:02:02.782000+00:00	In this step I am going to modify the already existing document which I have inserted in step 1. I will use same put method in the request. This was small CRUD operation which I tried to perform by…	en	https://miro.medium.com/…jr1YbyOIJY2w.png	Medium	https://medium.com/@anshumankaku/connecting-marklogic-with-python-94915ed30edc	I was always curious of connecting MarkLogic with data scientist favorite language “Python”. I got chance to explore in one of my recent projects which I was working on. Step 1: Inserting Document into MarkLogic using python We are using python request library for loading document. This step will insert a document into MarkLogic default database Step 2: Reading document from MarkLogic using python In this step we are getting document from MarkLogic by passing URI Step 3 Updating the already existing document in MarkLogic In this step I am going to modify the already existing document which I have inserted in step 1. I will use same put method in the request. Step 4 Deleting the document present in MarkLogic In this step I will delete the document present in MarkLogic This was small CRUD operation which I tried to perform by using REST API of MarkLogic. This is just the tip of the large iceberg in the development world, but it is good to start with. In future I will bring more stories related to Marklogic. Reference:
correct_foundationPlace_00033	FactBench	2	1	https://en.wikipedia.org/wiki/MarkLogic_Server	en	MarkLogic Server	https://upload.wikimedia…rklogic-logo.PNG	https://upload.wikimedia…rklogic-logo.PNG	[ "https://en.wikipedia.org/static/images/icons/wikipedia.png", "https://en.wikipedia.org/static/images/mobile/copyright/wikipedia-wordmark-en.svg", "https://en.wikipedia.org/static/images/mobile/copyright/wikipedia-tagline-en.svg", "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/Ambox_rewrite.svg/40px-Ambox_rewrite.svg.png", "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Marklogic-logo.PNG/250px-Marklogic-logo.PNG", "https://upload.wikimedia.org/wikipedia/en/thumb/8/8a/OOjs_UI_icon_edit-ltr-progressive.svg/10px-OOjs_UI_icon_edit-ltr-progressive.svg.png", "https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1", "https://en.wikipedia.org/static/images/footer/wikimedia-button.svg", "https://en.wikipedia.org/static/images/footer/poweredby_mediawiki.svg" ]	[]	[]	[ "" ]	null	[ "Contributors to Wikimedia projects" ]	2009-04-21T14:08:56+00:00		en	/static/apple-touch/wikipedia.png		https://en.wikipedia.org/wiki/MarkLogic_Server	MarkLogic ServerDeveloper(s)MarkLogicWritten inC, C++, JavaScriptAvailable inEnglishTypeDocument-oriented databaseWebsitewww .marklogic .com MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data.[1] MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors.[1] MarkLogic's customers are mostly Global 2000 companies. MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows.[1] Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document).[2] Banking[1] Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. 2003—Cerisent XQE 1.0 2004—Cerisent XQE 2.0 2005—MarkLogic Server 3.0 2006—MarkLogic Server 3.1 2007—MarkLogic Server 3.2 2008—MarkLogic Server 4.0 2009—MarkLogic Server 4.1 2010—MarkLogic Server 4.2 2011—MarkLogic Server 5.0 2012—MarkLogic Server 6.0 2013—MarkLogic Server 7.0 2015—MarkLogic Server 8.0: Ability to store JSON data and process data using JavaScript.[3] 2017—MarkLogic Server 9.0: Data integration across Relational and Non-Relational data. 2019—MarkLogic Server 10.0 2022—MarkLogic Server 11.0
correct_foundationPlace_00033	FactBench	1	33	https://github.com/marklogic-community/marklogic-healthcare-starter-kit	en	kit: The MarkLogic Healthcare Starter Kit (HSK) is a working project for a healthcare payer data hub, particularly geared toward service to Medicaid customers. Also called an operational data store (O	https://opengraph.githubassets.com/681c4094dc0c28759756a296ac0dd862365e0a0bac7a544fb8f8efedbc3ab63a/marklogic-community/marklogic-healthcare-starter-kit	https://opengraph.githubassets.com/681c4094dc0c28759756a296ac0dd862365e0a0bac7a544fb8f8efedbc3ab63a/marklogic-community/marklogic-healthcare-starter-kit	[]	[]	[]	[ "" ]	null	[ "marklogic-community" ]	null	The MarkLogic Healthcare Starter Kit (HSK) is a working project for a healthcare payer data hub, particularly geared toward service to Medicaid customers. Also called an operational data store (ODS), the HSK supports a mandate by the U.S. Centers for Medicare and Medicaid Services (CMS) to comply with the FHIR (Fast Healthcare Interoperability Resources) specifications for the electronic exchange of healthcare information. - marklogic-community/marklogic-healthcare-starter-kit	en	https://github.com/fluidicon.png	GitHub	https://github.com/marklogic-community/marklogic-healthcare-starter-kit	MarkLogic Healthcare Starter Kit Description & Purpose Get the Healthcare Starter Kit (HSK) Deploy the HSK Installation Steps: Using the HSK Using Data Hub Central Using Gradle Ingesting the Data Curating the Data Running Unit and Integration Tests Maintaining and Modifying the HSK Extending the HSK About the sample source data ml-gradle Data Hub Central and ml-gradle Deployment best practices and caveats: Loading the SNOMED-CT Ontology This README is intended as a short description of the project and instructions for getting set up and running. For more information on the project as a whole please refer to the Cookbook The MarkLogic Healthcare Starter Kit (HSK) is a working project for a healthcare payer data hub, particularly geared toward service to Medicaid customers. Also called an Operational Data Store (ODS), the HSK supports a mandate by the U.S. Centers for Medicare and Medicaid Services (CMMS) to comply with the Fast Healthcare Interoperability Resources (FHIR) specification for the electronic exchange of healthcare information. MarkLogic HSK is intended as a starting point for a healthcare data hub with working code, as well as sample data and configurations. It is also a good foundation for implementing FHIR-compliant data services when used in combination with the Marklogic FHIR Mapper. Users can upload raw, heterogeneous health records and use the harmonization features inherited by the HSK from the MarkLogic Data Hub to canonicalize and master their data. MarkLogic’s powerful default indexing and other Data Hub features make it easy to explore data and models to gain additional insight for future development and operations. Documentation for external projects, tools, and specifications referenced by this README are available as follows: MarkLogic Data Hub MarkLogic Server HL7/FHIR Clone the source or download a tagged release zip file from the MarkLogic HSK repository. The HSK was built and tested with the following prerequisites: Java 8 or 11 MarkLogic Data Hub Central v5.5.1 MarkLogic Server >= v10.0-7 Note: Installation steps assume a MarkLogic Server user/role with sufficient privileges is specified. Refer to the MarkLogic Data Hub documentation if needed. Download MarkLogic Data Hub Central using the link above Unzip the tagged release or clone the source into a directory of your choosing. At the top level of your project directory, change the mlUsername and mlPassword properties in gradle-local.properties to set your default user's username and password, based on the MarkLogic user you intend to use (admin, DrSmith, etc.). The project includes several sample demo users, such as DrSmith (password demo), who is capable of running all operations. Deploy Healtcare Starter Kit data hub: ./gradlew mlDeploy See Maintaining and Modifying the HSK below. ./gradlew mlLoadData Loads reference data input to user-defined steps and functions included with this project ./gradlew loadOntologies Loads ontologies for ICD10CM & ICD10PCS, and SNOMED-CT if it exists. There are two primary ways to access and use the deployed HSK. For GUI access, use MarkLogic Data Hub Central. For command line access, use gradle. A mix of these methods can be used as needed by your development requirements. See Maintaining and Modifying the HSK below for more information. In the top level of your project directory, run java -jar marklogic-data-hub-central-5.5.1.war At this point, you can use Data Hub Central to run the processing flows to ingest, curate, and explore the sample data and models provided. If you prefer using the CLI to run and test flows, you can use the premade tasks we have provided to ingest & harmonize data instead via the provided gradlew utility. To ingest all data you can run ./gradlew ingest, or to ingest a smaller set of claims (for faster setup) you can run ./gradlew ingestSmaller. If you would like to load sets of data individually you can run the tasks that the above depend on instead: To curate all previously ingested data you can run ./gradlew harmonizeAll. If you would like to curate sets of data individually you can run the tasks that the above depends on instead: ./gradlew harmonizeClaims ./gradlew harmonizeOrganizations ./gradlew harmonizePatients ./gradlew harmonizeProviders To verify the deployment, two test suites are provided. To run JUnit integration test of the complete flow from ingest to curation, use ./gradlew test To run MarkLogic Unit Tests (developed in server-side Javascript), use ./gradlew mlUnitTest The test suites can be found in the following project directories: JUnit integration: src/test/java/com/marklogic/hsk MarkLogic unit tests: src/test/ml-modules/root/test/suites The ClaimSuite is an example of a fully self-contained, independent test suite that can be run just after setup is done, without needing to load data. The other unit test suites are not necessarily configured to run independently of data load. See the Cookbook for more information on how to extend the HSK. As mentioned previously, this project is intended as a starting point for a healthcare data hub and provides many reusable functions & code modules. While most of the code is reusable, the sample data and ingestion/mapping steps will have to be replaced to work with your own data. The sample health population data provided in this project was generated using the Synthea synthetic health records project. It is included for illustration purposes only and should be replaced with your raw data files. The HSK project provides sample records for 755 patients and associated healthcare providers, organizations, claims, claims transactions, and payors. The Marklogic Gradle plugin (ml-gradle) provides the commands needed to deploy, maintain, test and modify the HSK. Full documentation can be found on the ml-gradle Wiki Data Hub Central (DHC) can be used to modify entities, run ingest and curation steps, explore content, and monitor jobs. Please note that when making changes using DHC, they are not propagated to the local project directory. You can run ./gradlew hubPullChanges to download the changes made in DHC and write them to your local project directory. ./gradlew hubPullChanges will overwrite any local changes you have made to Data Hub artifacts that were not pushed to the database using ./gradlew hubDeployUserArtifacts. Code modules and configuration will not be overwritten. If you happen to clear or delete all of your user data from the staging database, data-hub-STAGING, you will need to re-ingest the reference data by running ./gradlew mlLoadData This will restore the reference document contents found in the referenceData/ directory into the collection required to run user-defined steps included with the project. If your data does not use SNOMED-CT codes this section can be skipped If you need to load a SNOMED-CT Ontology into your HSK instance, you will need to download the ontology yourself as it requires a license for use and distribution.
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Get started today!	en	/favicon.ico?v=2	CData Software	https://www.cdata.com/	Office Depot Leans on CData to 'Lift & Shift' Critical Data and Enable Analytics Integration “In a very short amount of time we had the drivers installed, working, and building our analytics cubes on a daily basis. We installed the driver, we pointed the cubes at Snowflake using the driver, and we were up and going.” — Terry Campbell Office Depot Sr. IT Manager Read case study Holiday Inn Club Rests Easy with Error-Free Salesforce Data Movement from CData Sync “I can sleep again, knowing that the replication is working. If I stopped CData Sync today, I'd get flooded with calls from my teams in the next 20 minutes. The near-real-time data we get with Sync has transformed how we work in a big way.” — Irving Toledo Holiday Inn Club Vacations Senior Software Architect Read case study MyCoach Sport Saves Time and Manpower on Reporting “Before Connect Cloud, it took multiple people almost a full day to manually collect the data we needed to produce weekly reports in Google Data Studio. Now it's just a click of a button.” — Ali Moran MyCoach Sport Data Analyst Read case study
correct_foundationPlace_00033	FactBench	1	64	https://www.progress.com/	en	Develop, Deploy & Manage High-Impact Business Apps	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/home-persona-short.png?sfvrsn=707023b9_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/home-persona-long.png?sfvrsn=3978ccfa_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/home-bg-clear.jpg?sfvrsn=625dbcf6_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home-whats-new/genai-semaphore-webinar.png?sfvrsn=9dd5c2b6_5", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/rag-slider-img.png?sfvrsn=61bde1b9_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/sitefinity-r15-1.png?sfvrsn=3560adad_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home-whats-new/announces-appointment.png?sfvrsn=1ab2c009_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home-whats-new/gartner.png?sfvrsn=9dedefa_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home-whats-new/announces-appointment.png?sfvrsn=1ab2c009_3", "https://d117h1jjiq768j.cloudfront.net/images/default-source/default-album/progress-album/images-album/video-thumbnails-album/how-progress-impacts-our-lives-thumbnail3.png?sfvrsn=64e82717_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/home-footer-min.png?sfvrsn=c5e8151c_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/default-album/introducing-the-network.png?sfvrsn=b3bcb64c_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/blogs/2024/05-2024/whats-it-like-to-be-acquired-by-progress-part-2_1200-x-620.png?sfvrsn=583e9027_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/home/chef-courier-resource.png?sfvrsn=da4a1462_1", "https://d117h1jjiq768j.cloudfront.net/images/default-source/openedge-campaigns/oe-ddm-webinar-thumbnail-300x225.png?sfvrsn=45b5c2e1_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/datadirect-campaigns/resource-list-image-570x321.png?sfvrsn=7cdcb6b3_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/oe-campaigns/thumbnail-300x225333d9c93-89dc-40ff-9e6b-fd0d95f56e6c.png?sfvrsn=ec92d3bc_2", "https://d117h1jjiq768j.cloudfront.net/images/default-source/sf_local/exceptional-cx-sitefinity-insight.png?sfvrsn=ae217678_1" ]	[]	[]	[ "" ]	null	[]	null	Progress products speed business app development, automate processes to configure, deploy & scale apps, and make critical data more accessible and secure.	en	/favicon.ico?v=2	Progress.com	https://www.progress.com/	Progress, Telerik, Ipswitch, Chef, Kemp, Flowmon, MarkLogic, Semaphore and certain product names used herein are trademarks or registered trademarks of Progress Software Corporation and/or one of its subsidiaries or affiliates in the U.S. and/or other countries. Any other trademarks contained herein are the property of their respective owners. See Trademarks for appropriate markings.
correct_foundationPlace_00033	FactBench	1	72	https://stackshare.io/stackups/couchbase-vs-marklogic	en	What are the differences?	https://img.stackshare.i…089/KMIbGY8C.png	https://img.stackshare.i…089/KMIbGY8C.png	[]	[]	[]	[ "" ]	null	[]	null	Couchbase - Document-Oriented NoSQL Database. MarkLogic - Schema-agnostic Enterprise NoSQL database technology, coupled w/ powerful search & flexible application services.	en		StackShare	https://stackshare.io/stackups/couchbase-vs-marklogic
correct_foundationPlace_00033	FactBench	2	27	https://www.applytosupply.digitalmarketplace.service.gov.uk/g-cloud/services/468672822722444	en	MarkLogic Enterprise NoSQL Database Server (UK Government Edition)	https://www.applytosupply.digitalmarketplace.service.gov.uk/static/images/favicon.ico	https://www.applytosupply.digitalmarketplace.service.gov.uk/static/images/favicon.ico	[]	[]	[]	[ "" ]	null	[]	null		en	/static/images/favicon.ico		null	We use some essential cookies to make this service work. We’d also like to use analytics cookies so we can understand how you use the service and make improvements.
correct_foundationPlace_00033	FactBench	1	24	https://www.linkedin.com/posts/marklogic_data-hub-strategy-for-effective-ai-and-analytics-activity-7165415709760561152-ICj8	en	Progress MarkLogic on LinkedIn: Data Hub Strategy for Effective AI and Analytics Governance	https://media.licdn.com/dms/image/D4E22AQHJlkrLlKAWKg/feedshare-shrink_2048_1536/0/1708368082742?e=2147483647&v=beta&t=Im36ugYQEF6QMvNw-btgz-LYNGGELF47aBBzd7HdWzQ	https://media.licdn.com/dms/image/D4E22AQHJlkrLlKAWKg/feedshare-shrink_2048_1536/0/1708368082742?e=2147483647&v=beta&t=Im36ugYQEF6QMvNw-btgz-LYNGGELF47aBBzd7HdWzQ	[ "https://media.licdn.com/dms/image/D563DAQG5OYkkH4Ctcg/image-scale_191_1128/0/1721132895528/marklogic_cover?e=2147483647&v=beta&t=qTzlNGlaS6xRpl5qPUPAMY0NMH8x_tT1se3GH7Ockto" ]	[]	[]	[ "" ]	null	[ "Progress MarkLogic" ]	2024-02-19T18:43:52.169000+00:00	Join us this Thursday to learn how building a data hub can support effective data and AI governance. The webinar will cover: - What the critical components…	en	https://static.licdn.com/aero-v1/sc/h/al2o9zrvru7aqj8e1x2rzsrca		https://www.linkedin.com/posts/marklogic_data-hub-strategy-for-effective-ai-and-analytics-activity-7165415709760561152-ICj8	Join us this Thursday to learn how building a data hub can support effective data and AI governance. The webinar will cover: - What the critical components of data governance are - Why data privacy concerns matter - How to better secure critical information and proprietary data - Which governance-first capabilities to look for in a data management tool - How Progress MarkLogic can help you build a data hub and implement a data governance policy that fits your data Whatever your data strategy for 2024 looks like, having a strong data management foundation in place is imperative to put the guardrails on your data access. Register today ➡ https://lnkd.in/gbNkK2DB? #datagovernance #dataplatform #datahub #dataanalytics #datamanagement
correct_foundationPlace_00033	FactBench	2	88	https://kellblog.com/2013/12/01/the-pillorying-of-marklogic-why-selling-disruptive-technology-to-the-government-is-hard-and-risky/	en	The Pillorying of MarkLogic: Why Selling Disruptive Technology To the Government is Hard and Risky	https://s0.wp.com/i/webclip.png	https://s0.wp.com/i/webclip.png	[ "https://i0.wp.com/kellblog.com/wp-content/uploads/2021/09/Dave-Kellog-Headshot-for-Kellblog-min.png?resize=105%2C84&ssl=1", "https://i0.wp.com/i.creativecommons.org/l/by-nc-nd/4.0/80x15.png?w=500&ssl=1" ]	[ "https://www.scribd.com/embeds/188332853/content?start_page=1&view_mode&access_key=key-8qexnunxqq5zod3s4g" ]	[]	[ "" ]	null	[ "Dave Kellogg" ]	2013-12-01T00:00:00	There’s a well established school of thought that high-tech startups should focus on a few vertical markets early in their development. The question is whether government should be one of them? The government seems to think so. They run a … Continue reading →	en	https://s0.wp.com/i/webclip.png	Kellblog	https://kellblog.com/2013/12/01/the-pillorying-of-marklogic-why-selling-disruptive-technology-to-the-government-is-hard-and-risky/	There’s a well established school of thought that high-tech startups should focus on a few vertical markets early in their development. The question is whether government should be one of them? The government seems to think so. They run a handful of programs to encourage startups to focus on government. Heck, the CIA even has a venture arm right on Sand Hill Road, In-Q-Tel, whose mission is to find startups who are not focused on the Intelligence Community (IC) and to help them find initial customers (and provide them with a dash of venture capital) to encourage them to do so. When I ran MarkLogic between mid-2004 and 2010, we made the strategic decision to focus on government as one of our two key verticals. While it was then, and still is, rather contrarian to do so, we nevertheless decided to focus on government for several reasons. The technology fit was very strong. There are many places in government, including the IC, where they have a bona fide need for a hybrid database / search engine, such as MarkLogic. Many people in government were tired of the Oracle-led oligopoly in the RDBMS market and were seeking alternatives. (Think: I’m tired of writing Oracle $40M checks.) While this was true in other markets, it was particularly true in government because their problems were compounded by lack of good technical fit — i.e., they were paying an oligopolist a premium price for technology that was not, in the end, terribly well suited to what they were doing. Unlike other markets (e.g., Finance, Web 2.0) where companies could afford the high-caliber talent able to use the then-new open source NoSQL alternatives, government — with the exception of the IC — was not swimming in such talent. Ergo, government really needed a well-supported enterprise NoSQL system usable by a more typical engineer. The choice had always made me nervous for a number of reasons: Government deals were big, so it could lead to feast-or-famine revenue performance unless you were able to figure out how to smooth out the inherent volatility. Government deals ran through systems integrators (SI) which could greatly complexify the sales cycle. Government was its own tribe, with its own language, and its own idiosyncrasies (e.g., security clearances). While bad from the perspective of commercial expansion, these things also served as entry barriers that, once conquered, should provide a competitive advantage. The only thing I hadn’t really anticipated was the politics. It had never occurred to me, for example, that in a $630M project — where MarkLogic might get maybe $5 to $10M — that someone would try to blame failure of what appears to be one of the worst-managed projects in recent history on a component that’s getting say 1% of the fees. It makes no sense. But now, for the second time, the New York Times has written an article about the HealthCare.gov fiasco where MarkLogic is not only one of very few vendors even mentioned but somehow implicated in the failures because it is different. HealthCare.gov Let me start with a few of my own observations on HealthCare.gov from the sidelines. (Note that I, to my knowledge, was never involved with the project during my time at MarkLogic.) From the cheap seats the problems seem simple: Unattainable timelines. You don’t build a site “just like Amazon.com” using government contractors in a matter of quarters. Amazon has been built over the course of a more than a decade. No Beta program. It’s incomprehensible to me that such a site would go directly from testing into production without quarters of Beta. (Remember, not so long ago, that Google ran Beta’s for years?) No general oversight. It seems that there was no one playing the general contractor role. Imagine if you built a house with plumbers, carpenters, and electricians not coordinated by a strong central resource. Insufficient testing. The absent Beta program aside, it seems the testing phase lasted only weeks, that certain basic functionality was not tested, and that it’s not even clear if there was a code-freeze before testing. Late changes. Supporting the idea that there was no code freeze are claims that the functional spec was changing weeks before the launch. Sadly, these are not rare problems on a project of this scale. This kind of stuff happens all the time, and each of these problems is a hallmark of a “train wreck” software development project. To me, guessing from a distance, it seems pretty obvious what happened. Someone who didn’t understand how hard it to build was ordered up a website of very high complexity with totally unrealistic timeframes. A bunch of integrators (and vendors) who wanted their share of the $630M put in bids, probably convincing themselves in each part of the system that if things went very well that they could maybe make the deadlines or, if not, maybe cut some scope. (Remember you don’t win a $50M bid by saying “the project is crazy and the timeframe unrealistic.”) Everybody probably did their best but knew deep down that the project was failing. Everyone was afraid to admit that the project was failing because nobody likes to deliver bad news, and it seems that there was no one central coordinator whose job it was to do so. Poof. It happens all the time. It’s why the world has generally moved away from big-bang projects and towards agile methodologies. While sad, this kind of story happens. The question is how does the New York Times end up writing two articles where somehow the failure is somehow blamed on MarkLogic. Why is MarkLogic even mentioned? This the story of a project run amok, not the story of a technology component failure. Politics and Technology The trick with selling disruptive technology to the government is that you encounter two types of people. Those who look objectively at requirements and try to figure out which technology can best do the job. Happily, our government contains many of these types of people. Those who look at their own skill sets and view any disruptive technology as a threat. I met many Oracle-DBA-lifers during my time working with the government. And I’m OK with their personal decision to stop learning, not refresh their skills, not stay current on technology, and to want to ride a deep expertise in the Oracle DMBS into a comfortable retirement. I get it. It’s not a choice I’d make, but I can understand. What I cannot understand, however, is when someone takes a personal decision and tries to use it as a reason to not use a new technology. Think: I don’t know MarkLogic, it is new, ergo it is a threat to my personal career plan, and ergo I am opposed to using MarkLogic, prima facie, because it’s not aligned with my personal interests. That’s not OK. To give you an idea of how warped this perspective can get (and while this may be urban myth), I recall hearing a story that one time a Federal contractor called a whistle-blower line to report the use of MarkLogic on system instead of Oracle. All I could think of was Charlton Heston at the end of Soylent Green saying, “I’ve seen it happening … it’s XML … they’re making it out of XML.” The trouble is that these folks exist and they won’t let go. The result: when a $630M poorly managed project gets in trouble, they instantly raise and re-raise decisions made about technology with the argument that “it’s non-standard.” Oracle was non-standard in 1983. Thirty years later it’s too standard (i.e., part of an oligopoly) and not adapted to the new technical challenges at hand. All because some bright group of people wanted to try something new, to meet a new challenge, that cost probably a fraction of what Oracle would have charged, the naysayers and Oracle lifers will challenge it endlessly saying it’s “different.” Yes, it is different. And that, far as I can tell, was the point. And if you think that looking at 1% of the costs is the right way to diagnose a struggling $630M project, I’d beg to differ. Follow the money. ### FYI, in researching this post, I found this just-released HealthCare.gov progress report.
correct_foundationPlace_00033	FactBench	1	32	https://valuemomentum.com/career/marklogic-developer/	en	MarkLogic Developer	https://valuemomentum.co…3-03-360x360.jpg	https://valuemomentum.co…3-03-360x360.jpg	[ "https://valuemomentum.com/wp-content/themes/valuemomentum/assets/dist/img/refresh/logos/ValueMomentum-logo.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Digital-Cloud-Solutions-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/DataLeverage-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/CoreLeverage-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/QualityLeap-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/BizDynamics-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Digital-Cloud-Solutions-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/DataLeverage-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Customer-Communicatoin-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/QualityLeap-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Webinars-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Case-Studies-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Analyst-Reports-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Whitepapers-1.png", "https://valuemomentum.com/wp-content/uploads/2023/05/infographic.png", "https://valuemomentum.com/wp-content/uploads/2023/01/About-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Management-Team-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Board-of-Directors-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Memberships-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/Partnerships-1.png", "https://valuemomentum.com/wp-content/uploads/2023/01/What-they-say-1.png", "https://valuemomentum.com/wp-content/themes/valuemomentum/assets/dist/img/refresh/logo-knockout.png", "https://dc.ads.linkedin.com/collect/?pid=77915&fmt=gif" ]	[]	[]	[ "" ]	null	[]	2024-06-03T11:50:28+00:00	Job Title: Software Engineer/Senior software Engineer Primary Skill: NO SQL (Mark Logic, Mongo DB, Cassandra) Location: Hyderabad/Pune/Coimbatore Mode of Work: Work from Office Experience: 3-7 years. About the Job : We are looking for a Software Engineer who will be […]	en	https://valuemomentum.com/wp-content/themes/valuemomentum/assets/dist/img/icons/favicon.ico	ValueMomentum	https://valuemomentum.com/career/marklogic-developer/	Job Title: Software Engineer/Senior software Engineer Primary Skill: NO SQL (Mark Logic, Mongo DB, Cassandra) Location: Hyderabad/Pune/Coimbatore Mode of Work: Work from Office Experience: 3-7 years. About the Job : We are looking for a Software Engineer who will be responsible for Marklogic development and should be able to develop and support MarkLogic solutions. Know your team: At ValueMomentum’s Engineering Center, we are a team of passionate engineers who thrive on tackling complex business challenges with innovative solutions while transforming the P&C insurance value chain. We achieve this through strong engineering foundation and continuously refining our processes, methodologies, tools, agile delivery teams, and core engineering archetypes. Our core expertise lies in six key areas: Cloud Engineering, Application Engineering, Data Engineering, Core Engineering, Quality Engineering, and Domain expertise. Join a team that invests in your growth. Our Infinity Program empowers you to build your career with role-specific skill development leveraging immersive learning platforms. You’ll have the opportunity to showcase your talents by contributing to impactful projects. Responsibilities: Understand requirements and analyze. Understand the functional/non-functional requirements. Participate in client calls and prepare the clarification list to seek clarifications. Prepare the list of requirements and seek review input from the key stakeholders. Update requirement traceability matrix. Create an Impact analysis document to understand the impact on the existing functionality when required. Requirements: Bachelor’s degree in computer science, Mathematics, Engineering, or similar discipline or equivalent 2-6 years of total technical experience in NOSQL Database with Mark Logic in development team Strong technical experience in XML, XPATH, XQUERY, XSLT languages and unit testing, Code Versioning, and best practices Good Knowledge on Mark Logic Data Hub framework for ingestion and Harmonization Experience in content loading using MLCP and Bulk transformation using CORB tools. Should have good experience using ML Gradle or Roxy. Knowledge on semantics and triples Good knowledge on REST API’s and Mark Logic Modules Mark Logic 9 version experience and usage of TDE views is plus. Having experience in any NOSQL (Cassandra, Mongo DB) database is a plus. Domain Wise – Solution design in areas such as insurance. Excellent verbal and written communication skills. Strong analytical and problem-solving skills. Need to be excellent team player in Agile Methodology About ValueMomentum: ValueMomentum is a leading solutions provider for the global property & casualty insurance industry, supported by deep domain and technology capabilities. We offer a comprehensive suite of advisory, development, implementation, and maintenance services across the entire P&C insurance value chain. This includes Underwriting, Claims, Distribution, and more, empowering insurers to stay ahead with sustained growth, high performance, and enhanced stakeholder value. Trusted by over 75 insurers, ValueMomentum is one of the largest standalone insurance-focused solutions providers to the US insurance industry. Our culture – Our fuel At ValueMomentum, we believe in making employees win by nurturing them from within, collaborating and looking out for each other. People first – Empower employees to succeed. Nurture leaders – Nurture from within. Enjoy wins – Recognize and celebrate wins. Collaboration – Foster a culture of collaboration and people-centricity. Diversity – Committed to diversity, equity, and inclusion. Fun – Create a fun and engaging work environment. Warm welcome – Provide a personalized onboarding experience. Company perks & benifits:
correct_foundationPlace_00033	FactBench	1	49	https://blog.knoldus.com/how-marklogic-server-is-used-in-different-industries/	en	How MarkLogic Server is used in different industries	https://blog.knoldus.com…76-108506-1.webp	https://blog.knoldus.com…76-108506-1.webp	[ "https://www.knoldus.com/wp-content/uploads/Knoldus-logo-1.png", "https://blog.knoldus.com/wp-content/uploads/2023/02/nastech-logo.svg", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/united-states.png", "https://www.knoldus.com/wp-content/uploads/2021/12/canada.png", "https://www.knoldus.com/wp-content/uploads/2021/12/singapore.png", "https://www.knoldus.com/wp-content/uploads/2021/12/netherlands.png", "https://www.knoldus.com/wp-content/uploads/2021/12/european-union.png", "https://blog.knoldus.com/wp-content/uploads/2022/07/search_icon.png", "https://www.knoldus.com/wp-content/uploads/Knoldus-logo-1.png", "https://blog.knoldus.com/wp-content/uploads/2023/02/nastech-logo.svg", "https://www.knoldus.com/wp-content/uploads/bars.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2017/06/knoldus_blocklogo.png?fit=220%2C53&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/06/cloud-storage-banner-background_53876-108506-1.webp?fit=740%2C493&ssl=1", "https://secure.gravatar.com/avatar/3f1f4dc837a878d185f723515378d244?s=110&d=monsterid&r=g", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/Knoldus-logo-final.png?fit=1447%2C468&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/nashtech-logo-white.png?fit=276%2C276&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/IOSTQB-Platinum-Partner-white.png?fit=268%2C96&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/cmmi5-white.png?fit=152%2C84&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-27001-white.png?fit=120%2C113&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-27002-white.png?fit=120%2C114&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-9001-white.png?fit=120%2C114&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-lightbend-white.png?fit=151%2C32&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-databricks-white-.png?fit=133%2C20&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-confluent-white.png?fit=147%2C28&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-docker-white.png?fit=112%2C29&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-hashiCorp-white.png?fit=144%2C31&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-ibm-white.png?fit=63%2C25&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-daml-white.png?fit=107%2C29&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-datastax-white.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-kmine-white.png?fit=138%2C36&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-rust-foundation-white.png?fit=138%2C43&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-scala-white-1.png?fit=107%2C46&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-snowflake-white-1.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/umbraco-1.png?fit=178%2C50&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/aws-partner-logo-1.png?fit=92%2C56&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/Microsoft-Gold-Partner_white-1.png?fit=172%2C50&ssl=1" ]	[]	[]	[ "" ]	null	[ "Prakhar Rastogi" ]	2022-09-27T12:07:43+00:00	MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents in the data model for semantics.	en	https://blog.knoldus.com…2/04/favicon.png	Knoldus Blogs	https://blog.knoldus.com/how-marklogic-server-is-used-in-different-industries/	I will be walking through some of the cases studies and industry use cases that are being talked about in the official MarkLogic Solutions – https://www.marklogic.com/solutions/ MarkLogic Server currently operates in a variety of industries. Although the data retracted and extracted from MarkLogic differs in each sector, many customers have similar data management issues. Common issues include: Low cost Accurate and efficient search Enterprise-grade features Ability to store heterogeneous data from multiple sources in a single repository and make it immediately available for search Rapid application development and deployment Publishing/Media Industry BIG Publishing accepts data sources from publishers, wholesalers, and distributors and sells them information in data sources, web services, and websites, as well as through other proprietary solutions. Demand for the vast amount of information stored in the company’s database was high the company’s search solutions working with a conventional relational database were not effectively fulfilling that requirement. The company recognized that a new search solution was needed for customers to get relevant content from its huge database. The database had to handle 600,000 to 1 million updates per day while searching and when loading new content. The company was usually six to eight days behind schedule when a particular document would arrive at a time when it would be available to its customers. MarkLogic combines full-text search with the W3C standard XQuery language. MarkLogic the platform can simultaneously load, query, manipulate, and render content. When loading the content to MarkLogic, it is automatically converted to XML and indexed so it is instantly available for search. Hiring MarkLogic allowed the company to improve search capabilities through a combination of XML element query, XML proximity searching, and full-text search. MarkLogic’s XQuery interface searches the content and structure of XML data and facilitates access to XML content. It only took about 4 to 5 months company to develop solutions and implement them. Government / Public Sector XYZ Government wants to make it easier for county employees, developers, and residents to access real-time information about zoning changes, county ordinances, and property history. The county has volumes of data in different systems and in different formats the need to ensure more efficient access to data while maintaining the integrity of the recorded data. They need a solution that fits into their local IT infrastructure, can be implemented quickly and keeps hardware and license costs low and predictable. The solution is to migrate all existing PDF, Word, or CAD files from the county’s legacy systems to MarkLogic, which provides secure storage for all record data, easy-to-use search, and the ability to geospatially view results on a map. By centralizing their data in MarkLogic, district officials can access all the data they need from one central repository. MarkLogic allows the county to transform and enrich the data, as well as view and correlate it in a variety of ways using a variety of applications. Additionally, XYZ Government can make this information even more accessible to its constituents by deploying a publicly accessible web portal with powerful search capabilities on top of the same central MarkLogic repository. Financial Services Industry ABC Services Inc. provides financial research to customers on a subscription basis. Because every second counts in the fast-paced world of stock trading, the company needs to deliver new research to its subscribers as quickly as possible to help them make better decisions about their trades. Unfortunately, this effort was hampered by the company’s outdated infrastructure. Due to the shortcomings of the current tool, they were unable to easily respond to new requirements or fully utilize the documents being created. In addition, they could not meet their targets for timely delivery of alerts. ABC Services has replaced its legacy system with MarkLogic Server. Now the company can take full advantage of the information from the research. The solution significantly reduces alert latency and delivers information to the customer’s portal and email. In addition, the ability to create triple indexes and perform semantic searches greatly improved the user experience. With the new system, ABC Services provides timely research to 80,000 users worldwide, improving customer satisfaction and competitive advantage. By alerting customers more quickly to the availability of critical new research, financial traders gain a definitive edge in the office and on the trading floor. Other Industries Other industries benefiting from MarkLogic Server include: Government Intelligence — Identify patterns and discover connections from massive amounts of heterogeneous data. Airlines — Flight manuals, service records, customer profiles. Insurance — Claims data, actuary data, regulatory data. Education — Student records, test assembly, online instructional material. Legal — Laws, regional codes, public records, case files. References: http://www.marklogic.com/solutions/
correct_foundationPlace_00033	FactBench	2	0	https://en.wikipedia.org/wiki/MarkLogic	en	MarkLogic	https://en.wikipedia.org/static/favicon/wikipedia.ico	https://en.wikipedia.org/static/favicon/wikipedia.ico	[ "https://en.wikipedia.org/static/images/icons/wikipedia.png", "https://en.wikipedia.org/static/images/mobile/copyright/wikipedia-wordmark-en.svg", "https://en.wikipedia.org/static/images/mobile/copyright/wikipedia-tagline-en.svg", "https://upload.wikimedia.org/wikipedia/en/thumb/b/b4/Ambox_important.svg/40px-Ambox_important.svg.png", "https://upload.wikimedia.org/wikipedia/commons/thumb/9/93/MarkLogic_logo.svg/220px-MarkLogic_logo.svg.png", "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Increase2.svg/11px-Increase2.svg.png", "https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1", "https://en.wikipedia.org/static/images/footer/wikimedia-button.svg", "https://en.wikipedia.org/static/images/footer/poweredby_mediawiki.svg" ]	[]	[]	[ "" ]	null	[ "Contributors to Wikimedia projects" ]	2005-09-23T23:26:05+00:00		en	/static/apple-touch/wikipedia.png		https://en.wikipedia.org/wiki/MarkLogic	American software company MarkLogic is an American software business that develops and provides an enterprise NoSQL database, which is also named MarkLogic. They have offices in the United States, Europe, Asia, and Australia. In February 2023, MarkLogic was acquired by Progress Software for $355 million.[2] Overview[edit] Founded in 2001 by Christopher Lindblad and Paul Pedersen, MarkLogic Corporation is a privately held company with over 500 employees[3] that was acquired by Vector Capital in October 2020.[4] The company claims to have over 1,000 customers, including Chevron, JPMorgan Chase, Erie Insurance Group, Johnson & Johnson, and the US Army.[5] MarkLogic has gotten positive reception by multiple tech newsletters.[6][7][8] History[edit] MarkLogic was originally named Cerisent when it was founded in 2001[9] by Christopher Lindblad, who was the Chief Architect of the Ultraseek search engine at Infoseek, as well as Paul Pedersen, a professor of computer science at Cornell University and UCLA, and Frank R. Caufield, Founder of Darwin Ventures,[10] to address shortcomings with existing search and data products. The product first focused on using XML document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. In 2009, IDC mentioned MarkLogic as one of the top Innovative Information Access Companies with under $100 million in revenue.[11] In May 2012, Gary Bloom was appointed as Chief Executive Officer.[12] He held senior positions at Symantec Corporation, Veritas Software, and Oracle.[13] Post-acquisition, the company named Jeffrey Casale as its new CEO. Funding[edit] MarkLogic received its first financing of $6 million in 2002 led by Sequoia Capital, followed by a $12 million investment in June 2004, this time led by Lehman Brothers Venture Partners.[14] The company received additional funding of $15 million in 2007 from its existing investors Sequoia and Lehman.[14] The same investors put another $12.5 million into the company in 2009.[15] On 12 April 2013, MarkLogic received an additional $25 million in funding, led by Sequoia Capital and Tenaya Capital.[16][17] On May 12, 2015, MarkLogic received an additional $102 million in funding, led by Wellington Management Company, with contributions from Arrowpoint Partners and existing backers, Sequoia Capital, Tenaya Capital, and Northgate Capital. This brought the company's total funding to $173 million and gave MarkLogic a pre-money valuation of $1 billion.[18] NTT Data announced a strategic investment in MarkLogic on 31 May 2017.[19] Products[edit] Further information: MarkLogic Server The MarkLogic product is considered a multi-model NoSQL database for its ability to store, manage, search JSON and XML documents and semantic data (RDF triples). Releases[edit] 2001 – Cerisent XQE 1.0[citation needed] 2004 – Cerisent XQE 2.0[citation needed] 2005 – MarkLogic Server 3.0[citation needed] 2006 – MarkLogic Server 3.1 2007 – MarkLogic Server 3.2 2008 – MarkLogic Server 4.0 2009 – MarkLogic Server 4.1 2010 – MarkLogic Server 4.2 2011 – MarkLogic Server 5.0 2012 – MarkLogic Server 6.0 2013 – MarkLogic Server 7.0 2015 – MarkLogic Server 8.0: Ability to store JSON data and process data using JavaScript.[20] 2017 – MarkLogic Server 9.0: Data integration across Relational and Non-Relational data. 2019 – MarkLogic Server 10.0 Licensing and support[edit] MarkLogic is proprietary software, available under a freeware developer software license or a commercial "Essential Enterprise" license.[21] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. Technology[edit] MarkLogic is a multi-model NoSQL database that has evolved from its XML database roots to also natively store JSON documents and RDF triples for its semantic data model. It uses a distributed architecture that can handle hundreds of billions of documents and hundreds of terabytes of data.[citation needed] MarkLogic maintains ACID consistency for transactions and has a Common Criteria certification security model, high availability, and disaster recovery. It is designed to run on-premises within public or private cloud computing environments like Amazon Web Services.[22] MarkLogic's Enterprise NoSQL database platform is utilized in various sectors, including publishing, government and finance. It is employed in a number of systems currently in production.[22] See also[edit] Document database Graph database Multi-model database NoSQL Triple store MongoDB References[edit] Further reading[edit]
correct_foundationPlace_00033	FactBench	2	92	https://blog.nashtechglobal.com/how-to-bring-the-data-and-document-in-marklogic/	en	How to bring the data and document in MarkLogic	https://i0.wp.com/blog.n…000%2C1333&ssl=1	https://i0.wp.com/blog.n…000%2C1333&ssl=1	[ "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/04/nashTechLogo-red-.png?fit=320%2C320&ssl=1", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/05/solution-menu.png?fit=206%2C101&ssl=1", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Code-quality.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Cloud-engineering-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/data-solutions.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/AI-ML-icons.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Application-engineering-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Maintenance-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Business-process-solutions-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Quality-solutions-icons.svg", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/05/solution-menu.png?fit=206%2C101&ssl=1", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/commitment.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/communication.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Business-process-solutions-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Quality-solutions-icons.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/data-solutions.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/AI-ML-icons.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Cloud-engineering-icon.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/05/Code-quality.svg", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/05/solution-menu.png?fit=206%2C101&ssl=1", "https://blog.nashtechglobal.com/wp-content/plugins/elementor/assets/images/placeholder.png", "https://blog.nashtechglobal.com/wp-content/plugins/elementor/assets/images/placeholder.png", "https://blog.nashtechglobal.com/wp-content/plugins/elementor/assets/images/placeholder.png", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/05/news-placeholder.webp?fit=1024%2C384&ssl=1", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2023/04/title-square.gif?fit=512%2C464&ssl=1", "https://secure.gravatar.com/avatar/8b51ab708ea17efbb44a96c4977a17ed?s=300&d=identicon&r=g", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2024/01/visualizing-data-abstract-purple-background-with-motion-blur-digital-data-analysis-concept-5.jpg?fit=1024%2C682&ssl=1", "https://i0.wp.com/i.postimg.cc/3RCxH6ZG/Untitled-document-2.jpg?resize=640%2C480&ssl=1", "https://i0.wp.com/i.postimg.cc/3rZFw0Fk/Screenshot-from-2022-11-11-20-03-40.png?resize=678%2C300&ssl=1", "https://i0.wp.com/i.postimg.cc/wjFbSb7j/Screenshot-from-2022-11-11-20-05-16.png?w=1300&ssl=1", "https://i0.wp.com/i.postimg.cc/Nj37C3cX/Screenshot-from-2022-11-11-20-12-06.png?resize=481%2C322&ssl=1", "https://i0.wp.com/i.postimg.cc/CKWQqbgV/knoldus-blog-footer-banner.jpg?w=1300&ssl=1", "https://secure.gravatar.com/avatar/8b51ab708ea17efbb44a96c4977a17ed?s=300&d=identicon&r=g", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2024/07/pexels-photo-356056.jpeg?fit=768%2C510&ssl=1", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2024/07/react.jpg?fit=768%2C512&ssl=1", "https://i0.wp.com/blog.nashtechglobal.com/wp-content/uploads/2024/07/people-watching-and-monitoring-data-1.png?fit=768%2C576&ssl=1", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/nashtech-logo.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/Great-place-to-work.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/clutch-global-pmwg48jqr16isxjvair3mf9nhvv7u19tfs5x0h2nnc.png.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO_27001-1.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/IOSTQB-Platinum-Partner_white-1.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/cmmi5-logo-473BEF2C9F-seeklogo-1.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-27001.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-27002.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-9001.svg" ]	[]	[]	[ "" ]	null	[ "Khalid Ahmed" ]	2022-11-14T05:00:00+00:00	MarkLogic brings all the features you need into one unified system as it is the only Enterprise NoSQL database. MarkLogic can bring multiple heterogeneous data sources into a single platform architecture, allowing for homogenous data access. For bringing the data we need to insert the documents. On the query console, we are able to perform […]	en	https://i0.wp.com/blog.n…it=32%2C32&ssl=1	NashTech Insights	https://blog.nashtechglobal.com/how-to-bring-the-data-and-document-in-marklogic/	Khalid Ahmed Table of Contents MarkLogic brings all the features you need into one unified system as it is the only Enterprise NoSQL database. MarkLogic can bring multiple heterogeneous data sources into a single platform architecture, allowing for homogenous data access. For bringing the data we need to insert the documents. On the query console, we are able to perform the query according to requirements. Bringing in the documents There are many ways to insert documents into a MarkLogic database. Available interfaces include: MarkLogic Data Hub MarkLogic Content Pump Apache Nifi REST API XQuery functions MuleSoft Data Movement SDK (Java API) Node.js API JavaScript functions Apache Kafka Content Processing Framework XCC WebDAV Explanation of available interfaces MarkLogic Data Hub: The MarkLogic Data Hub is open-source software that is used to inject data from different sources or from multiple sources. It is used to import the data as well as harmonize the data. MarkLogic Content Pump: It is a command line tool for bulk loading billions of documents into a MarkLogic database, extracting or copying the content. It helps us to make workflow integration very easy. Apache Nifi: It is useful when someone needs to ingest data from a relational database into a MarkLogic Database. REST API: It provides a programming language agnostic way to write a document in MarkLogic. XQuery functions: When we want to write the document to a MarkLogic database then this function is used. Able to write the records from the query console or from the XQuery application. MuleSoft: The Marklogic connector for MuleSoft is Used to bring data from various other systems into the MarkLogic database. Available Interfaces Data Movement SDK (Java API): Included in the java API, the data movement SDK provides the classes for java developers to use to import and transform documents. Node.js API: It provides Node.js classes for the developers to use to write the document to a MarkLogic database from their Node.js code. JavaScript functions: Able to write the document through the query console or by using the javascript application. Apache Kafka: When we need to stream the data into the database, we can do it by using the Kafka MarkLogic connector. Content Processing Framework: A Pipeline framework for making changes to documents as they are being loaded into the database, such as enriching the data or transforming the PDF or MS office document in XML. XML Contentbase Connector (XCC): If you need to create a multi-tier application that communicates with the MarkLogic then it is useful. WebDAV: Web Distributed Authoring and Versioning used to drag and drop the documents in the Marklogic Database. Inserting the document using the Query Console To insert the document using the query console javaScript or XQuery used. The xdmp.documentLoad() function. Used to load the document from the file system into a database. declareupdate(); xdmp.documentLoad("path of the source file") Running a JavaScript expression that makes changes to a database. Need to use the declareUpdate function. The xdmp.documentinsert() function is used to write a document into a database. declareUpdate(); xdmp.documentInsert('/employee1.json', { 'title : 'Knoldus' , 'description': 'Amazing place to work' }); Uniform Resource Identifier (URI) To address any document in a MarkLogic database, it is necessary that each document has a unique URI. /products/1.json The URI does not refer to the physical location of a document in a database. Provides a unique name for referencing the document. Deleting the documents The clear button in the admin interface can be used to delete all the documents in a database. To delete an individual document, the xdmp.documentDelete() function can be used. declareUpdate(); xdmp.documentDelete('/employee1.json'); Accessing a Document To read a document in a database, use the cts.doc(). cts.doc('/employee1.json); Modifying Documents Documents can be modified via various APIS and tools, including data hub, JavaScript, XQuery, etc. JavaScript functions for updating documents include: xdmp.nodeReplace() xdmp.nodeInsert() xdmp.nodeInsertBefore() xdmp.nodeInsertAfter() xdmp.nodeDelete() Conclusion MarkLogic is a NoSql database with many facilities and if someone wants to insert the data then this blog is helpful. After insertion needs to access and modify the document by using some predefined functions. Reference: https://docs.marklogic.com/guide/ingestion/intro https://docs.marklogic.com/guide/concepts/data-management https://www.udemy.com/course/marklogic-fundamentals/learn/lecture/4793940#overview Share this: Like this: Like Loading... Suggested Article Khalid Ahmed
correct_foundationPlace_00033	FactBench	1	90	https://www.mapquest.com/us/virginia/marklogic-corp-380819073	en				[]	[]	[]	[ "" ]	null	[]	null		en	/favicon.ico		null
correct_foundationPlace_00033	FactBench	1	86	https://sourceforge.net/software/product/MarkLogic/	en	MarkLogic	https://a.fsdn.com/allura/s/marklogic/icon?34362ec2a92bdb119f1955ece9d2e6c3961cc8f1dc759ba71dca4d7f1ed8fc21	https://a.fsdn.com/allura/s/marklogic/icon?34362ec2a92bdb119f1955ece9d2e6c3961cc8f1dc759ba71dca4d7f1ed8fc21	[ "https://a.fsdn.com/con/images/sandiego/sf-logo-full.svg", "https://a.fsdn.com/con/images/sandiego/sf-logo-full.svg", "https://a.fsdn.com/con/images/sandiego/sf-logo-full.svg", "https://a.fsdn.com/con/images/sandiego/sf-ad-unit.png", "https://a.fsdn.com/allura/s/marklogic/icon?34362ec2a92bdb119f1955ece9d2e6c3961cc8f1dc759ba71dca4d7f1ed8fc21?&w=90", "https://a.fsdn.com/allura/s/marklogic/icon?34362ec2a92bdb119f1955ece9d2e6c3961cc8f1dc759ba71dca4d7f1ed8fc21?&w=90", "https://a.fsdn.com/allura/s/bigquery/icon?079e6341e7a7905bf09fa30ecee0e9e94fcd80f203c0fd747b0a5cc78ba483a7?&w=90", "https://a.fsdn.com/allura/s/domo/icon?c308575de82b51333f0a1d3c5b5bfc4955bd02cad09fbb155f45dba99074b440?&w=90", "https://a.fsdn.com/allura/s/timi/icon?4543b2b47015cb1bffeb36125be1f50bde775c3221b80fa9871a4c56640a1ca1?&w=90", "https://a.fsdn.com/con/app/proj/marklogic.s/screenshots/Captura%20de%20pantalla%202022-10-21%20112246.png/245/183/1", "https://a.fsdn.com/con/app/proj/marklogic.s/screenshots/Captura%20de%20pantalla%202022-10-21%20112252.png/245/183/1", "https://a.fsdn.com/allura/s/pentaho-business-analytics/icon?1697208685?&w=90", "https://a.fsdn.com/allura/s/scalegrid/icon?1659961718?&w=90", "https://a.fsdn.com/allura/s/manta-flow/icon?1c839d785f4f735db2e1f62a31790200f8c7ed80abdcce6d734e07762496249c?&w=90", "https://a.fsdn.com/allura/s/amazon-dynamodb/icon?1709152214?&w=90", "https://a.fsdn.com/con/app/nel_img/15657", "https://a.fsdn.com/allura/s/ibm-db2/icon?722409e7e771a0abc5aa7d76be1a1076efcc8faaf47aac34579c4714a99db5ca?&w=48", "https://a.fsdn.com/allura/s/sap-hana/icon?1716229795?&w=48", "https://a.fsdn.com/allura/s/pentaho-business-analytics/icon?1697208685?&w=48", "https://a.fsdn.com/allura/s/entellifusion/icon?1601470483?&w=48", "https://a.fsdn.com/allura/s/ibm-cloud-pak-for-data/icon?5c6834fe8c50f9234d7624ad6c2237db99a3947e208789fb4c612e9b650bec5a?&w=48", "https://a.fsdn.com/allura/s/data-integration/icon?1656947339?&w=48", "https://a.fsdn.com/allura/s/infongen/icon?87f744e690eaf1082f8dfdc5e9bc59850f3038c54b5812d07861495624dd0ebc?&w=48", "https://a.fsdn.com/allura/s/ibm-db2/icon?722409e7e771a0abc5aa7d76be1a1076efcc8faaf47aac34579c4714a99db5ca?&w=48", "https://a.fsdn.com/allura/s/sap-hana/icon?1716229795?&w=48", "https://a.fsdn.com/allura/s/pentaho-business-analytics/icon?1697208685?&w=48", "https://a.fsdn.com/con/images/sandiego/sf-logo-full.svg", "https://sourceforge.net/software/visit?idsite=1&rec=1&rand=76161&dimension2=pg_commercial_billboard&url=https%3A%2F%2Fsourceforge.net%2Fsoftware%2Fproduct%2FMarkLogic%2F&action_name=MarkLogic+Reviews+and+Pricing+2024&dimension1=MarkLogic" ]	[]	[]	[ "MarkLogic", "MarkLogic pricing", "MarkLogic reviews" ]	null	[]	null	Learn about MarkLogic. Read MarkLogic reviews from real users, and view pricing and features of the Database software	en	//a.fsdn.com/con/img/sandiego/logo-180x180.png	SourceForge	https://sourceforge.net/software/product/MarkLogic/	Progress Software Get Quote Need help deciding? Talk to one of our software experts for free. They will help you select the best software for your business. Get A Quote First Name * Last Name * Business E-mail * z Phone * Company * Job Title * I understand by clicking on "GET QUOTE" below I am agreeing to the SourceForge Terms of Use and the Privacy Policy which describe how we use and share your data. I agree to receive quotes and related information from SourceForge.net and our partners via phone calls and e-mail to the contact information I entered above. I understand that I can withdraw my consent at anytime. Please refer to our Terms of Use and Privacy Policy or Contact Us for more details. JavaScript is required for this form. Submitting... Alternatives to MarkLogic Claim this page Google Cloud BigQuery (1587 Ratings) BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Learn more Domo (1358 Ratings) Domo puts data to work for everyone so they can multiply their impact on the business. Our cloud-native data experience platform goes beyond traditional business intelligence and analytics, making data visible and actionable with user-friendly dashboards and apps. 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Thanks to an original & unique software infrastructure, TIMi is optimized to offer you the greatest flexibility for the exploration phase and the highest reliability during the production phase. TIMi is the ultimate “playground” that allows your analysts to test the craziest ideas! Learn more Audience Organizations looking for a powerful platform that unlocks data agility About MarkLogic Unlock data value, accelerate insightful decisions, and securely achieve data agility with the MarkLogic data platform. Combine your data with everything known about it (metadata) in a single service and reveal smarter decisions—faster. Get a faster, trusted way to securely connect data and metadata, create and interpret meaning, and consume high-quality contextualized data across the enterprise with the MarkLogic data platform. Know your customers in-the-moment and provide relevant and seamless experiences, reveal new insights to accelerate innovation, and easily enable governed access and compliance with a single data platform. MarkLogic provides a proven foundation to help you achieve your key business and technical outcomes—now and in the future. Pricing Free Version: Free Version available. Free Trial: Free Trial available. Integrations Ratings/Reviews Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software Company Information Progress Software Founded: 1981 United States www.marklogic.com Pentaho (2 Ratings) Accelerate data-driven transformation powered by intelligent data operations across your edge to multi-cloud data fabric. Pentaho lets you automate the daily tasks of collecting, integrating, governing, and analytics, on an intelligent platform providing an open and composable foundation for all enterprise data. Schedule your free demo to learn more about Pentaho Integration and Analytics, Data Catalog and Storage Optimizer. Learn more ScaleGrid (3 Ratings) ScaleGrid is a fully managed Database-as-a-Service (DBaaS) platform that helps you automate your time-consuming database administration tasks both in the cloud and on-premises. Easily provision, monitor, backup and scale your open source databases with high availability, advanced security, full superuser and SSH access, query analysis, and troubleshooting support to improve the performance of your deployments. Supported databases include: - MySQL - PostgreSQL - Redis™ - MongoDB® database - Greenplum™ (coming soon) The ScaleGrid platform supports both public and private clouds, including AWS, Azure, Google Cloud Platform (GCP), DigitalOcean, Linode, Oracle Cloud Infrastructure (OCI), VMware and OpenStack. Used by thousands of developers, startups, and enterprise customers including Atlassian, Meteor, and Accenture, ScaleGrid handles all your database operations at any scale so you can focus on your application performance. Learn more MANTA Manta is the world-class automated approach to visualize, optimize, and modernize how data moves through your organization through code-level lineage. By automatically scanning your data environment with the power of 50+ out-of-the-box scanners, Manta builds a powerful map of all data pipelines to drive efficiency and productivity. Visit manta.io to learn more. With Manta platform, you can make your data a truly enterprise-wide asset, bridge the understanding gap, enable self-service, and easily: • Increase productivity • Accelerate development • Shorten time-to-market • Reduce costs and manual effort • Run instant and accurate root cause and impact analyses • Scope and perform effective cloud migrations • Improve data governance and regulatory compliance (GDPR, CCPA, HIPAA, and more) • Increase data quality • Enhance data privacy and data security Learn more Amazon DynamoDB (1 Rating) Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It's a fully managed, multi-region, Multimaster, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and can support peaks of more than 20 million requests per second. Many of the world's fastest-growing businesses such as Lyft, Airbnb, and Redfin as well as enterprises such as Samsung, Toyota, and Capital One depend on the scale and performance of DynamoDB to support their mission-critical workloads. Focus on driving innovation with no operational overhead. Build out your game platform with player data, session history, and leaderboards for millions of concurrent users. Use design patterns for deploying shopping carts, workflow engines, inventory tracking, and customer profiles. DynamoDB supports high-traffic, extreme-scaled events. Learn more You Might Also Like Unlock the Power of Web Intelligence Collect public data at scale with industry-leading web scraping solutions See your detailed proxy usage statistics, easily create sub-users, whitelist your IPs, and conveniently manage your account. Do it all in the Oxylabs® dashboard. Save your time and resources with a data collection tool that has a 100% success rate and does all of the heavy-duty data extraction from e-commerce websites and search engines for you. With our provided solutions and the best proxies, focus on data analysis rather than data delivery. We make sure that our IP proxy resources are stable and reliable, so no issues occur during scraping jobs. We continuously work on expanding the current proxy pool to fit every customer's needs. Our clients & customers can reach out to us at any time, and we respond to their urgent needs around the clock. Choose the best proxy service and we’ll provide all the support you need. We want you to excel in scraping jobs, so we share all the know-how we have gathered over the years. Product Details Platforms Supported SaaS Windows Mac iPhone iPad Android Training Documentation Live Online Webinars In Person Support Phone Support 24/7 Live Support Online MarkLogic Frequently Asked Questions Q: What kinds of users and organization types does MarkLogic work with? MarkLogic works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup. Q: What languages does MarkLogic support in their product? MarkLogic supports these languages: English. Q: What kind of support options does MarkLogic offer? MarkLogic offers support via phone support, 24/7 live support, and online. Q: What other applications or services does MarkLogic integrate with? MarkLogic integrates with: AWS IoT SiteWise, Fosfor Refract, Hackolade, IRI Voracity, Knowi, Lyftrondata, Microsoft Power Query, NetOwl EntityMatcher, NetOwl Extractor, NetOwl NameMatcher, NetOwl TextMiner, Pandora FMS, StarfishETL, and SYSTRAN. Q: Does MarkLogic have a mobile app? Yes, MarkLogic has a mobile app for Android, iPhone, and iPad. Q: What type of training does MarkLogic provide? MarkLogic provides training in the form of documentation, live online, webinars, and in person. Q: Does MarkLogic offer a free trial? Yes, MarkLogic offers a free trial. MarkLogic Product Features Data Fabric Data Networking / Connecting Data Collaboration Data Analytics Persistent Data Management No Data Redundancy Data Access Management Metadata Functionality Data Lineage Tools Database Backup and Recovery Creation / Development Data Migration Data Replication Data Search Data Security Database Conversion Mobile Access Monitoring NOSQL Performance Analysis Queries Relational Interface Virtualization NoSQL Database Auto-sharding Automatic Database Replication Data Model Flexibility Deployment Flexibility Dynamic Schemas Integrated Caching Multi-Model Performance Management Security Management MarkLogic Additional Categories
correct_foundationPlace_00033	FactBench	1	69	https://www.ncsi.com/event/uscentcom/marklogic/	en	MarkLogic	https://www.ncsi.com/wp-…m_1500x350-2.jpg		[ "https://www.ncsi.com/wp-content/uploads/2020/07/2020-USCentcom_1500x350-2.jpg", "https://www.ncsi.com/wp-content/uploads/2020/07/2020-USCentcom_1200x400.jpg", "https://www.ncsi.com/wp-content/uploads/2020/07/2020-USCentcom_700x400.jpg", "https://www.ncsi.com/wp-content/uploads/2019/02/MarkLogic-Logo-Confirmed-12.03.2020-1024x207.png", "https://www.ncsi.com/wp-content/uploads/2021/01/Bill-Washburn-MarkLogic-300x300.jpg" ]	[]	[]	[ "" ]	null	[]	2021-01-26T09:04:59-05:00		en		NCSI - Government Event Planner - Government Conferences, Government Events, Military Expos	https://www.ncsi.com/event/uscentcom/marklogic/	Presented by: Dr. Matthew Johnson, CDAO; CDR Michael Hanna, ONI The Deputy Secretary of Defense has said that Responsible AI is how we will win with regard to strategic competition, ‘not in spite of our values, but because of them’…but what does this actually mean? This presentation introduces the DoD’s work to operationalize this approach, showing how Responsible AI sustains our tactical edge. The presentation provides a deep dive into a key piece of the DoD’s approach to Responsible AI: the Responsible AI Toolkit. The Toolkit is a voluntary process through which AI projects can identify, track, and mitigate RAI-related issues (and capitalize on RAI-related opportunities for innovation) via the use of tailorable and modular assessments, tools, and artifacts. The Toolkit rests on the twin pillars of the SHIELD Assessment and the Defense AI Guide on Risk (DAGR), which holistically address AI risk. The Toolkit enables risk management, traceability, and assurance of responsible AI practice, development, and use. Moderator: Mr. Peter Teague, CDAO Panelists: Mr. Jon Elliott, CDAO; Dr. Shannon Gallagher, CMU SEI; Dr. Catherine Crawford, IBM, Mr. Shiraz Zaman, Nand AI A key problem with leveraging AI is understanding how it will integrate with existing workflows. I push this notion of understanding human parity in a given task so that we know what to expect when the model is deployed – i.e., we have performance parameters determined. However, with comprehensive capabilities, like LLMs, there may be multiple steps in a workflow that get replaced and we need to understand the impact of this. Moderator: LtCol Jeffrey Wong, CDAO Panelists: Dr. Kathleen Fisher, DARPA; Dr. Andrew Moore, Lovelace AI; Mr. Peter Guerra, Oracle The rise of LLMs over the past year has accelerated the development of AI and educated the public about the potential of this powerful technology. It has also flagged some of the problems inherent in complex, data-centric systems, to the point where many noted data scientists have questioned the wisdom of progressing too fast. What have LLMs taught us about the future of AI? How does this technology change the trajectory or expectation of new technology development? Moderator: Dr. Diana Gehlhaus, Special Competitive Studies Project Panelists: Ms. Jennifer Schofield, DAIM; Rear Adm. Alexis Walker, NRC; MajGen William Bowers, MCRC The question is not whether DoD needs digital talent, but rather how to get it, grow it, keep it—and how to use it most effectively. We’ll discuss the challenges facing DoD, including those systemic to the entire tech ecosystem, as well as those unique to DoD. We’ll explore ideas for addressing these challenges, and debate their pros, cons, and feasibility. There is no easy answer, but we’ll come away with a better sense of the options and trade space available to DoD. Moderator: Mr. David Jin, CDAO Panelists: Dr. Beat Buesser, IBM; Dr. Nathan VanHoudnos, CMU SEI; Mr. Alvaro Velasquez, DARPA As DoD systems become integrated with AI and autonomy capabilities, the question of novel attack surfaces and vulnerabilities arises. While adversarial AI has become a topic of great interest in recent years, much of the existing work within the field of adversarial AI has been done within academia and research. This panel discussion will bring together DoD adversarial AI experts to discuss the realistic application of adversarial AI on the DoD’s AI-enabled capabilities. Moderator: Dr. Robert Houston, CDAO Panelists: Mr. Evan Jones, UMD ARLIS ; Mr. Yosef Razin, IDA; Ms. Amber Mills, JHU-APL This panel emphasizes the importance of Human Systems Integration (HSI) Test and Evaluation (T&E) throughout the lifecycle of an AI-enabled system, advocating for its implementation early, often, and always. Traditional HSI T&E data is usually captured through discrete experiments, an approach not well-suited for the automated, continuous testing required for AI/ML models. The panel will discuss (1) the challenges in instrumenting HSI-relevant data capture, (2) strategies and methodologies for integrating HSI into automated, real-time testing environments, and (3) innovative measures that utilize real-time user inputs such as search queries, tone of voice, response latency, and sentiment analysis. Moderator: Ms. Margie Palmieri, CDAO Panelists: Dr. Michael Horowitz, OSD Policy; Lieutenant Colonel Kangmin Kim, ROK Army; Commodore Rachel Singleton, UK, Head, Defence AI Centre; Military Expert 6 Wilson Zhang, Singapore, Deputy Future Systems & Technology Architect The United States works closely with allies and partners to apply existing international rules and norms and develop a common set of understandings among nations guiding the responsible use of AI in defense. This panel provides the opportunity to promote order and stability in the midst of global technological change. The United States has been a global leader in responsible military use of AI and autonomy, with the Department of Defense championing ethical principles and policies on AI and autonomy for over a decade. Among various national and international efforts, the United States, together with 46 nations, endorsed the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy in November 2023, providing a normative framework addressing the use of these capabilities in the military domain. Given the significance of responsible AI in defense and the importance of addressing risks and concerns globally, the internationally focused session at the Symposium will be focused on these critical global efforts to adopt and implement responsible AI in defense. This panel will provide various country perspectives on the development, adoption, and implementation of principles and good practices on responsible AI, including multilateral efforts related to the Political Declaration on the Responsible Military Use of Artificial Intelligence and Autonomy. Presenter: Ricky Clark, NIH In May 2021, President Biden issued an executive order to strengthen and improve America’s cybersecurity. Known as “Zero Trust” the order called for federal agencies to wall off information technology (IT) systems behind a secure network perimeter. Two years later, federal agencies are “on the clock” and actively working to integrate Zero Trust architecture into their existing IT environment. According to a recent report from General Dynamics Information Technology (GDIT), the “Agency Guide to Zero Trust Maturity,” civilian and federal agencies are making progress toward meeting zero trust but continue to face significant challenges in implementation, such as lack of IT expertise, identifying and prioritizing needs and concerns around repairing or rebuilding existing legacy infrastructure. With a September 2024 deadline looming for compliance, what can agencies do to ensure they are compliant in 2024. During this session, NIH Information Technology Acquisition and Assessment Center (NITAAC) will explore the barriers agencies face in realizing zero trust and identify solutions that exist within the confines of the NITAAC Government-wide Acquisition Contracts (GWAC)s. The session will discuss the following: • Overview of Zero Trust • Common barriers agencies face • Practical solutions within the NITAAC GWACs to help overcome them Presenter: John Lee, NGA Software is key to almost every NGA mission, which means NGA must provide its developers with the best tools to build, release, and operate software securely at the speed of mission. NGA’s Common Operations Release Environment (CORE) seeks to answer that requirement by providing a shared environment with a collection of integrated development and operational services for teams inside and outside of NGA. The beginning of CORE dates back to 2016, when NGA first delivered a modern Platform-as-a-Service for teams to build on. The capabilities grew over the years. Today’s version of CORE gives software development teams a common toolset to build software more reliably, efficiently, and securely on all domains. CORE currently has seven service offerings—DevSecOps, Platform-as-a-Service, API Management, Developer Experience, Continuous Monitoring, Workflow Orchestration, and Messaging—with ML Ops coming soon. This presentation will provide an overview of CORE services and how adoption of the CORE is facilitating fulfillment of the NGA Software Way strategy, as well as give some examples of mission capabilities delivered to operations through the CORE. Presenter: Graig Baker, DISA DISA SD43 National Gateway Branch provides a range of assured messaging and directory services to a customer community that includes the Military Services, DoD Agencies, Combatant Commands (CCMD), and Other U.S. Government Agencies (OGA) and the Intelligence Community (IC). DISA is preparing to field the Organizational Messaging Service Java Messaging Service (OMS-JMS), a cutting-edge messaging and directory support solutions and services implementing the IC Message Service (ICMS) XML standard for hi-fidelity message formatting while continuing to support legacy ACP-127/128 gateway connections to provide seamless interoperability across our customer community for the preservation of National Defense. This presentation provides the messaging community an overview of the new DISA OMS-JMS solutions and services which are to begin fielding during FY24. Presenters: Katie Kalthoff, DIA; Jonathan Abolins, DIA; Joshua Burke, DIA DIA Platform-as-a-Service (DPaaS) is an enterprise container management platform that provides an open ecosystem to build, integrate, and enhance applications and services to meet requirements for production mission capabilities. Containerized applications hosted on DPaaS environments benefit from scalability, built-in security, hybrid-IT capabilities, and infrastructure-agnostic deployments. DPaaS enhances a developer’s ability to focus on functionality, enabling mission applications to be rapidly prototyped, deployed, and moved at the speed of mission while reducing technical overhead. DPaaS is also a leading force in DIA’s effort to provide compute and storage services at Edge locations. DPaaS enables application developers to build once and deploy everywhere, meaning to multiple networks as well as to the Edge. Edge deployments are a necessity in the era of strategic competition where warfighters and decision-makers must be able to quickly access data and applications in low-bandwidth or disconnected areas. DPaaS is pushing deployments to regional and edge locations to enable mission support while making applications easier to manage. Edge deployments allow for fewer service disruptions to forward deployed intelligence personnel and continued operations during disconnected events. This greater flexibility and ability to meet mission need will be a driving factor for greater innovation within IC application development. Presenter: Charles Bellinger, NGA As part of NGA’s greater multi-tiered edge strategy, Joint Regional Edge Nodes (JREN) and Odyssey systems—designed to facilitate the movement of critical intelligence and data sharing—are being deployed to combatant commands. JREN is an innovative, highly scalable, next-generation edge node capability providing the foundation to support Sensor to Effect (S2E) and future ground architecture with multiple cloudlike layers to enable seamless interoperability and collaboration in both connected and disconnected states. Deployed in January 2022, JREN provides significant storage, computing power, transport bandwidth, and applications closer to the tactical edge. JREN will support expanding DoD, IC, and coalition customer requirements with AOR-specific content, GEOINT/partner applications, and high-performance computing. Odyssey is a forward-deployed system that provides access to applications and theater GEOINT data hosted on local servers to support users at the edge in the event of disconnected ops. Using a combination of hardware, apps, data, and products, Odyssey deployments are available via a web browser established on theater users’ networks and connected back to NGA. This presentation will focus on design considerations such as increased resiliency in Denied, Degraded, Intermittent, and limited bandwidth (DDIL) environments via direct satellite downlink; reduced transport latency; and use of NGA’s Common Operations Release Environment to develop, deploy, and operate modern GEOINT software. This presentation will also highlight how automation, artificial intelligence, and other JREN and Odyssey services are prepared for the exponential growth in intelligence sensors and collection capabilities. Presenter: Vanessa Hill, DIA In today’s digital age, websites and applications have become an integral part of our daily lives and the digital landscape has transformed the way we interact with the world. However, not all users have the same abilities, and it is crucial to ensure that digital experiences are inclusive and accessible to everyone, including those with disabilities. DIA’s first-ever 508 IT Accessibility lab promotes a more inclusive and diverse digital environment, where everyone can participate and benefit from digital experiences by ensuring products are usable and accessible to all users. Come join us to learn how DIA is developing and testing capabilities, such as improved closed captioning on multiple platforms (VTC, SVTC, and DVTC) to leveraging virtual desktop to host a lightweight application that provides translation capabilities to support DIA’s multilingual Deaf and Hard of Hearing (DHH) members, and more. Incorporating accessibility testing into your digital product development process, and embracing the power of accessibility testing and training, unleashes the full potential of your digital products and creates a more inclusive digital environment for all users. Presenters: Jonathan Abolins, DIA; Katie Kalthoff, DIA; Joshua Burke, DIA Hybrid IT provides a solution that combines the capabilities of commercial cloud, government-owned data centers, and edge devices into one single capability. By using Hybrid IT, the Defense Intelligence Enterprise gains the flexibility to leverage the advantages of each service model to address the needs of different mission sets. A mix of cloud and on-prem provides improved disaster recovery capabilities, higher availability, and the ability to access mission-critical applications and data from anywhere, even in disconnected locations. However, hybrid and multi-cloud architectures pose unique security challenges and require a different approach than what solely on-prem environments or single clouds require. Without additional protections, we face the risk of fragmented security solutions and a decrease in threat visibility. The Defense Intelligence Agency protects enterprise and customer applications with a security service mesh which provides zero-trust enabled capabilities such as authorization and access control, network segmentation, end-to-end encryption, and continuous monitoring. The application networking layer provides baked-in security from development to production and enables threat monitoring across fragmented application networks and clouds. Presenters: Kevin Shaw, Guidehouse; Christine Owen, Guidehouse The Executive Order on Improving the Nation’s Cybersecurity (EO-14028) was released over two and a half years ago. While the EO rapidly accelerated programs across the federal government, we are now in a position to reflect and look to the future of Zero Trust. We will share lessons learned from real-life Zero Trust deployments (including what has worked, what hasn’t) and how organizations can and should continually evolve and adapt their program. Presenter: Bailey Bickley, NSA Defense Industrial Base (DIB) companies are relentlessly targeted by our adversaries, who seek to steal U.S. intellectual property, sensitive DoD information and DIB proprietary information to undermine our national security advantage and economy. NSA is working to contest these efforts by providing no-cost cybersecurity services to qualifying DIB companies. NSA’s services are designed to help protect sensitive, but unclassified, DoD information that resides on private sector networks by hardening the top exploitation vectors that foreign malicious actors are using to compromise networks. Eradicating cybersecurity threats to the DIB is an NSA priority. NSA’s Cybersecurity Collaboration Center (CCC) provides no-cost cybersecurity solutions for qualifying DIB companies. These solutions are easily implemented and scalable to protect against the most common nation-state exploitation vectors and are designed to help protect DoD information and reduce the risk of compromise. These services include Protective DNS, attack surface management, and access to NSA non-public, DIB-specific threat intelligence. Our pilot program is evaluating additional services for release. Hundreds of industry partners of all sizes and complexities have already signed up for NSA’s cybersecurity services, which has helped protect these networks against malicious cyber activity. The no-cost cybersecurity services have also assisted with the early identification, exposure, and remediation of multiple nation-state campaigns targeting the DIB. Presenter: Andrew Heifetz, NGA With the rise of Commercial Cloud Environment (C2E), programs have the potential to use services from multiple Cloud Service Providers (CSPs). Multiple CSPs can decrease cost through competition and increase innovation by providing exquisite and unique services. However, developing for a multiple cloud environment is fraught with challenges including data gravity/portability, lack of interoperability standards, multiple cloud knowledge gaps, and security accreditation. In order to address these challenges and prepare for C2E, NGA conducted several multiple cloud pilots and will share the lessons learned as well as recommendations to prepare for multiple cloud development. This presentation is important for anyone considering multiple clouds and hybrid environments. Moderator: Bob Crawford Panelists: Randy Resnick, DoD; David Voelker, DoN; Jennifer Kron, NSA; Ben Phelps, ODNI; Evan Kehayias, NGA This session is essential for attendees responsible for or in roles related to defending against the growing, sophisticated Cyber threats the DoD and IC face. To strengthen our defenses, a Zero Trust Architecture (ZTA) will be implemented across the DoD and IC. To enable this, sound strategies with support from a ZT Architecture (ZTA) will help to guide the DoD and IC to accomplish Zero Trust maturity from basic, to intermediate, to ultimately advanced levels over the next five years. The Office of the Intelligence Community Chief Information Officer (OIC CIO) developed a comprehensive Zero Trust (ZT) strategy and framework. The framework was developed by the IC ZT Steering Committee (ZTSC) and approved by all 18 IC elements. This session will focus on the tenets of the framework to include 31 capabilities, 4 maturity models, 7 pillars, and the IC ZT Architecture. DoD has developed their own robust Zero Trust framework. Working collaboratively the IC and DoD must implement Zero Trust, improving overall Cybersecurity while maintaining interoperability and data sharing capabilities. In this panel discussion, cybersecurity experts from the DoD and IC will discuss both the challenges and opportunities to significantly improve information protection capabilities and implementations by adopting the Zero Trust approach — “never trust, always verify, assume breach” — to protect U.S. national security assets. Presenters: Marissa Snyder, DIA; Lauren Hix, DIA; Lisa Schrenk, DIA Vintage is in, but not when it comes to payroll and benefits. Operating in a 20+ year-old IT system, DIA’s Office of Human Resources (OHR) current processes are overly complex, manual, and siloed. This resulted in incomplete, inconsistent datasets and slow reaction times to pivot the HR apparatus to mission needs. Even more importantly, this has taken DIA employees away from mission by burdening them with mundane administrative tasks. Soon, all of this will fade into history (like disco)! Propelled through the HR Modernization investment, we’ve taken revolutionary steps to transform DIA’s HR infrastructure to strengthen DIA’s mission posture for strategic competition. We invite you to learn more about our efforts and how we’ve gleaned helpful, data-driven insights from various studies of our workforce, networking with Department of Defense (DoD) and Intelligence Community (IC) partners, and engaging with commercial entities. This transformative shift requires a whole-of-agency cultural change to scale our capabilities for future needs. The modernization and overhaul of DIA’s HR is centered around creating exceptional employee experiences, reducing process timelines, increasing data quality and transparency. Cutting through the chaos created by a constrained and outdated infrastructure, HR Modernization is enabling DIA to put the right people in the right place, with the right skills needed to execute the mission. Presenter: John Boska, DIA Many government processes are lengthy and time-consuming, including the process of taking an application from development to production on government hosted networks. This poses a problem for mission-critical applications for which speed and efficiency is essential for getting information to intelligence personnel in the era of strategic competition. DIA’s Capability Delivery Pipeline (CDP) was created to simplify and modernize application development in the IC. CDP is a streamlined software development pipeline which embraces the DevSecOps methodology and industry standards. CDP will streamline the Authority to Operate (ATO) process, incentivize continuous integration and delivery (CI/CD), and abstract much of the overhead that comes with developing and deploying applications – including built-in security, governance, and hosting. CDP’s strategic goal is to provide one ecosystem used for secure software, hardware, service development, testing, and deployment spanning DIA’s Unclassified (IL5), Secret (IL6), and Sensitive Compartmented Information (SCI) networks. CDP also aims to bring in more cloud service providers to DIA to allow for infrastructure-agnostic development and reduce costs of development by eliminating duplicate services and capabilities. This pipeline will enable max capability for DIA customers and stakeholders and increase information sharing with agency partners and foreign allies. Ultimately, CDP empowers DIA to accelerate the delivery of capabilities and services to obtain a competitive advantage against our adversaries. Moderator: Ramesh Menon Panelists: Robert Lawton, ODNI; Dr. Abby Fanlo, CDAO; Elham Tabassi, NIST As AI becomes increasingly more prevalent and advanced, the potential to positively impact every sector of our society has become apparent. While AI technologies have created tremendous efficiencies in how we live, think, and choose to invest our time and energy, it also has the potential to harm those that use it if not properly managed. The risks can become especially high when AI is used for critical national security missions. As the Department of Defense (DoD) and Intelligence Community (IC) continue to adopt AI as a disruptive technology used to advance warfighting and intelligence gathering capabilities, it is imperative that we trust AI that is being used for these critical national security missions. On this panel, you will hear from experts spearheading the AI Ethics initiatives that will affect industry, DoD, and IC. Topics discussed will include the new AI Risk Management Framework, DoD Ethical AI Principles, and how these will affect how we use and create trustworthy AI systems. Panelists include AI Ethics experts from the Chief Digital and Artificial Intelligence Office and National Institute of Standards and Technology. This panel will be moderated by DIA’s Chief Technology Officer, Mr. Ramesh Menon. Moderator: Sudhir Marreddy Panelists: James Long, NGA; Ben Davis, ODNI; Amy Heald, CIA; Dylon Young, OUSD (I&S) This session will be a must-attend breakout for attendees to gain an understanding and perspective of the emerging technologies that present both threats and opportunities for U.S. national security. The panelists will include participants from both the DD and IC covering rapidly emerging technology areas such as AI/ML, Cloud, Cybersecurity/Zero Trust, Data, Digital Foundations, Interoperability, Networks, and more. With adversaries on the cusp of surpassing the U.S. in the near future, challenging our technological leadership, this panel will discuss the existential threat of rapidly emerging technologies. We will explore how we can both protect U.S. national security and prevent our adversaries from gaining access to, acquiring, developing and advancing their capabilities while we leverage those same capabilities. Presenters: Col Michael Medgyessy, USAF DAF CLOUDworks provides Enterprise and Security Services (IaaS), Platform as a Service (PaaS) and Collaboration tools (SaaS) to the DoD and AF IC. Partnered with Platform One, we provide DevSecOps pipelines across the Unclass, Secret and Top Secret cloud environments. Using our Operational DevSecOps for ISR NEXGEN (ODIN) platform enables your developers to focus on your application instead of underlying infrastructure. Our enterprise services reflect the security guardrails our Authorizing Official set forth. We are constantly iterating and adding common services to bring max value to our customers across the DoD and IC. Presenter: Dan Hetrick, ODNI Building clarity into a shared vision by defining the chaos. What does DEIA have to do with aligning a workforce? Diversity, Equity, Inclusion, Accessibility. Regardless of how one sees the message of DEIA, amazing potential rises by aligning organizational mission with DEIA principles. This presentation will highlight 10 ways to begin building a mindset under the Universal Principles within DEIA that will create a vision that drives mission to produce these benefits (at minimum), including better informed leaders in tune with the workforce, effective decision making, a shared vision that everyone supports, better products usable by everyone, Innovation, Security, Risk Mitigation, effective succession planning, and finally… A model of excellence for everyone to follow! Moderator: Shannon Paschel Panelists: Elciedes Dinch-Mcknight, DIA; Katie Lipps, DIA; Dr. Rosemary Speers, DIA ; Lori Wade, DIA CIO is trying to foster a growth mindset to drive organizational change in culture and structure by making a concerted effort to develop and promote leaders from within and to fully utilize the talents of executive women for more diverse leadership. Addressing barriers and challenges experienced from various types of discrimination and bias based on the intersection of gender, race, and other personal characteristics. CIO Women in Leadership Program showcases a panel of women leaders who share their experiences and successful strategies to advance their careers at DIA-CIO. A key to success for women to achieve Senior Executive Levels at CIO is allyship and advocacy. According to research and organizational best practices, inclusive behaviors and communication patterns from all employees and leaders create inclusive organizational cultural change. Presenters: Sonny Hashmi, GSA; Brian Shipley, Navy; Chris Hamm, GSA Government procurement is often a complicated business. Between budget issues, Federal Acquisition regulations (FAR), and mission-critical needs, getting the products and services you need in a timely and straightforward manner is challenging at best. Hear from customers and users who balance these requirements every day and help make it easier to get technology to the mission at the speed of need. The discussion will focus on the acquisition space and how partnerships between federal agencies can make it easier to rapidly field emerging technologies and do business with and across government. Presenter: Stephen Kensinger, DIA DIA is taking a holistic approach in reviewing and modernizing all of its provided services for Zero Trust to support the demands for its future data-centric architecture. This discussion will include how the agency is approaching Zero Trust to be a mission enabler for the Enterprise. This DIA vision includes efforts to streamline the Risk Management Framework (RMF) by integrating results through Zero Trust enabled technology/services and modernized processes. Although focus has been for near term maturity requirements, the team has started to explore the integration of machine learning to contribute to this streamlining effort. It will also delve into the planning and prototype efforts that the DIA Zero Trust team has led for development and integration of core cyber services to provide entitlements access to properly tagged data objects. The DIA Zero Trust team has partnered with DIA mission stakeholders and our Chief Data Office to begin to address these challenges and to convey to the workforce the new value these modernized DoDIIS services will offer to mission. Presenter: Robert Williams, DIA The Defense Intelligence Agency’s Analytic Innovation Office will discuss the AI Roadmap for All-source Analysis, which adds clarity and cohesiveness to the all-source analytic modernization process. The Roadmap provides a comprehensive and applied approach to artificial intelligence (AI) that spans experimentation, quality and tradecraft assurance, AI skills and digital literacy development, and business process improvements – aspects that were largely fragmented until now. The Roadmap achieves clarity from chaos by tightly aligning six key objectives that address the application of applied AI methods to mission, building an AI-ready analytic workforce, and equipping AI practitioners with a framework for ensuring compliance with analytic tradecraft standards. Hear about the critical challenges such as systematically upskilling an analytic workforce, accelerating the development of an AI-ready workforce by reducing the skills gap with low code solutions, and assessing analytic workflows at-scale to identify optimal human-machine-teaming opportunities. Other challenges include accessing data in ways that enables the leveraging of machine learning methods at-scale, and pivoting from reactionary to predictive analytics. You will hear about aspects of AI adoption through the lens of an organization responsible for leading analytic modernization, that will leave the audience and industry participants with an appreciation for the unique challenges of achieving AI-readiness within an all-source analytic organization. Presenters: Peter Guerra, Oracle; Josh Tatum, Oracle Tactical edge capabilities enable organizations to extend cloud services and applications to the edge. This allows for improved performance, security, and availability of applications and services, as well as to collect and analyze data at the edge, which can provide real-time insights and decision-making capabilities in connected and disconnected environments. Tactical edge capabilities, across classification boundaries, allow the warfighter to obtain situational awareness through edge compute, AI, and security where needed. This talk will walk through the use of tactical edge within the DoD and IC to present real world use cases. Presenters: Theresa Kinney, NASA; Kanitra Tyler, NASA; Jeanette McMillian, ODNI; Lisa Egan, DIA US Government Employees Only. Welcome to “The Exchange”; an internal, selective government-only community of intelligence and non-Title 50 agencies dedicated to initiating practices that help secure government-wide supply chains. It is where agencies and programs demonstrate and share their best practices towards mobilizing unique agency missions and authorities to mitigate risk. This panel of community members will inform and educate USG participants of opportunities and resources to help them secure IT supply chains at their agencies; moving from the Chaos of Risks and Threats to the Clarity of Actions that help address active management of supply chain risk. Presenters: Ben Davis, ODNI; Ron Ripper, ODNI; Colonel Christian Lewis, ODNI The Intelligence Community Information Environment (IC IE) and the Department of Defense Information Network (DoDIN) underpin IC and DoD missions. Today, we are more dependent on and also more vulnerable to attacks on assets in cyberspace than we have ever been. The benefits of emerging and over-the-horizon technologies are immense, but also introduce new attack vectors for malicious cyber actors. The partnership between the IC Security Coordination Center (IC SCC) and Joint Forces Headquarters DoDIN (JFHQ-DoDIN) is vital to defending the Nation’s most secure networks and critical national security information. Both organizations will discuss their mission, their partnership, and seek opportunities to extend the partnership to the broader USG, and harness the power and expertise our industry partners bring to bear. Looking to increase your data sharing and help your data find a new mission user base? Do you have limited data acquisition resources and want to take advantage of what the DoD and IC already have to offer? Explore how IC Data Services can assist your Agency/Organization to make your data discoverable, accessible, usable, and interoperable. IC Data Services, an ODNI Service of Common Concern, is foundational to enabling IC organizations to move forward on IC Data Strategy and component data strategy, gaining organizational efficiencies and mission outcomes in the process. Presenters: Katie Lipps, DIA; Marlene Kovacic, DIA Are you an industry provider of hardware, software, and/or services? Come learn how you can partner with DIA to protect yourselves from threats posed by adversaries in order to become a stronger and more secure partner supporting Agency and CIO top initiatives. This session will focus on what elements of your organization you need to be focusing on, high level concepts you can implement, and how your improved security posture benefits your partnership with DIA. As part of the DoDIIS Conference this year, NASA SEWP has been authorized to offer attendees an exclusive, in-person training session bringing Government agencies and industry providers together to dig into the world of SEWP. Pre-registration is required and is only available to participants of the DoDIIS Conference. During this training you will be able to explore emerging federal acquisition trends and gain valuable insights about our diverse range of products and services directly from the SEWP Program Management Office (PMO). We are delighted to offer a comprehensive demonstration of our cutting-edge web tools. This engaging session will equip you with the most up-to-date knowledge and ensure you are fully proficient in utilizing our advanced online resources. We want to empower you with the tools you need to succeed and stay ahead of the curve. This training is designed for both newcomers to SEWP and those seeking a refresher. Don’t worry if you’re unfamiliar with SEWP; we’ll guide you every step of the way. Plus, your attendance will earn you 4.0 Continuous Learning Points (CLPs) It’s an opportunity you definitely don’t want to miss! 10:00am – 12:30pm: Training Session (please arrive a few minutes early to be checked in prior to the training) Pre-Registration is required and limited to 100 participants! Reserve your space here. In this fireside chat we are going to have a conversation with two of the DoD’s premier R&D organization’s senior leaders. We will be covering topics such as SAP IT, cybersecurity, risk, mission, and policy. You are going to want to come to this chat to understand how well we are communicating at the most senior levels, where our community can do better, what keeps them up at night, and the challenges imposed by R&D. Derek Claiborne, Chainalysis Jackie Koven, Chainalysis Web3 is all about innovation and collaboration – but with that comes heightened risks. Chainalysis has a commitment to creating a safer environment for all who enter the world of Web3. In this discussion, we will explore blockchain’s potential in addressing challenges faced by our warfighters. The evolving threat landscape involving strategic competitors, rogue nations, and terrorist groups is examined, with a particular emphasis on their exploitation of cryptocurrencies for illicit activities. The role of blockchain technology in countering these threats is then elucidated, showcasing its characteristics like decentralization and transparency. This includes a deep dive into using blockchain for geolocating threat actors and tracking illicit activities. International collaboration and the integration of blockchain-based intelligence into defense strategies are discussed as well. Challenges, considerations, future prospects, and recommendations for blockchain adoption in cybersecurity and defense form vital segments of the discourse, ultimately underlining the significance of embracing emerging technologies like blockchain to empower warfighters and enhance national security in an ever-evolving digital landscape. Audiences will gain a comprehensive understanding of how blockchain technology can effectively address blockchain-enabled threats and enable the geolocation of threat actors in the realm of cybersecurity and defense. They will also recognize the pivotal role of international collaboration and blockchain integration in bolstering national security efforts across evolving global challenges. Harry Cornwell, Palo Alto Networks Delivering zero trust at an enterprise level begins with a fundamental change in how the DoD builds its cyber security architecture to prioritize both security and performance. Zero trust is built upon the foundation that there is already a malicious actor or compromised data or devices within the enterprise. This assumption creates a need for a process of continuous validation of users, devices, applications, and data in an entirely controlled and visible manner. With Palo Alto Networks’s Zero Trust Network Access 2.0 (ZTNA 2.0), coarse-grained access controls based on an “allow and ignore” model is left behind to introduce a consistent least-privilege access control model focusing on application layer security inspection. Josef Allen, USAF Adam Gruber, Applied Insight Those defending our nation depend on access to accurate, timely information – and must manage large amounts of data from more sources now than at any other point in history. Disparate data sources, networks, and classification levels currently make it impossible for users in SAP and CAP environments to view data within a single standardized and normalized lens, limiting mission agility and increasing the time between data ingest and incorporation into command decisions. To overcome these limitations, mission teams must currently develop custom tools and rely on manual processing of information to aggregate data and inform decisions. Feature gaps in pre-existing cloud capabilities within SAP environments further inhibit Guardians and other teams from efficiently leveraging cutting-edge technological capabilities to satisfy mission requirements, such as real-time data streaming, access to native cloud resources, and multi-cloud capabilities. Providing holistic data processing in SAP environments presents three major challenges: data transfer across and between classification fabrics, data access governance, and multi-tenancy. Additionally, implementing a fully comprehensive Zero Trust Architecture is paramount. This problem is complex, but with the right tools it is solvable. To accelerate data sharing to mission teams in a Common SAP across classification fabrics and disparate networks, USSF built a highly scalable, multi-tenant, ATO’d environment – empowering program teams to migrate critical mission workloads to the cloud while maintaining logical separation of those workloads. Additionally, the USSF team designed and implemented a cutting-edge data management capability that enforces Zero Trust access to data assets leveraging a cloud-based architecture. Douglas Gourlay, Arista Networks In this presentation, we delve into the challenges and possible solutions when designing a unified, multi-domain network architecture that seamlessly integrates a diverse range of platforms: GEO & LEO satellites, airborne platforms, terrestrial networks, GovCloud transit, and trans-oceanic cables. This architecture not only ensures dynamic, encrypted, and secure multi-access networks, but also incorporates a self-healing fabric that can adapt to signal-denied environments while reducing operational load. Complementing this vision, we will explore the paradigm shift from legacy network operating models towards a software-centric ‘modern operating model’. Here, configurations are procedurally generated by automation that incorporate variables from multiple discrete systems-of-record. We also simulate network changes in a virtual twin environment, deploy to the network upon completion, and generate comprehensive documentation of the change. The National Institute of Standards (NIST) has released several Post Quantum Cryptographic Algorithms planned for standardization in 2024. The National Security Agency has announced the Commercial National Security Algorithm (CNSA) Suite 2.0. The executive branch has released NSM-10. What does this mean for the SAP community? Dr. Whitfield Diffie, Dr. Robert Campbell, and Mr. Charles Robinson will discuss what this means for SAP program managers and how they can effectively plan for the upcoming migration to post quantum cryptography. The Panel will discuss current and past cryptography role outs.. The panel will discuss process, landscape, and do a deep dive of the underlying cryptography. The panel will explore past cryptographic migrations best practices and discuss what’s different now. A discussion on what government organizations should be aware of when migrating to the new Post Quantum Cryptography Algorithms. We will discuss of best practices guidelines that NIST NcCOE program is developing to support implementation and transformation of government IT environments. Finally, some consideration of the strategy and a tactical construct SAP program managers should consider when migrating to a Quantum Safe enterprise. In this session, we will delve into the transformative impact of Infrastructure as Code (IaC) models on modernizing network operations within the Department of Defense and Intelligence Community. The focus will be on leveraging procedural generation and IaC models for creating networking configurations, coverage-guided automated testing, and self-generating documentation. These techniques, integrated across a next-generation WAN, Campus, and Data Center reduce the complexity inherent in traditional networking configuration. This approach fosters the creation of repeatable design patterns that automate efficiently at scale and facilitate the generation of digital twin environments for functional testing and staging deployments. Then we will discuss and demonstrate a practical application of these models and technologies in deploying and operating a global WAN, encrypted with quantum-safe/secure cryptography, with trusted and measured/attested secure booting of each router, and utilizing a combination of networks including geostationary and commercial low-earth orbital satellites, LTE/5G, free-space photonics, public and private MPLS services, dark fiber and wavelength services, submarine transoceanic cables, and cloud provider backbones. Artificial Intelligence and Machine Learning (AI/ML) applications in cybersecurity sensing are heavily focused on threat detection by identifying abnormal indicators and eliminating false positives. The mathematical techniques used to achieve this have converged, with most applications still focused on perfecting existing algorithms. However, there are many aspects of human cognition which are not captured by AI/ML algorithms as they are applied today. Creativity, intuition, contextualization, topology, and even the special theory of relativity are emerging perspectives for AI/ML. New approaches are critical to “level up” our current sensing tools, and create the next generation of advanced artificial intelligence-driven cybersecurity. In most discussions about the digital divide, we’re referring to the fact that approximately one-third of the world’s population lacks access to the internet. We often associate it with developing countries and attribute it to factors such as economics and infrastructure. The negative consequence of this digital divide in the information age is that we leave behind individuals and entire communities. As cloud technologies become central to everyday life, that divide grows wider. Ironically, although the SAP community works on the most bleeding-edge technologies for our warfighters, it also suffers from being on the wrong side of a similar digital divide. In this session, we’ll look at how we can close the digital divide for the SAP community. Scott Devitt, General Dynamics Mission Systems Brian Newson, General Dynamics Mission Systems The GDMS Chief Engineer for Multilevel Security, Scott Devitt, will demonstrate and explore real-world SAP use cases with MLS containers for DE Environments. During his 37 years with General Dynamics, Scott has designed, built, installed, and maintained classified capabilities for the DoD and IC including operational mission cells supporting forward locations with multiple stove piped networks at different classification levels. His presentation will highlight the value of a DE polyinstantiated or containerized framework in safeguarding SAP data and the benefits of leveraging a multilevel file share when working across multiple connected classified environments. It will also discuss the challenges faced in integrating the innovative capability into legacy stovepipe SAP networks with existing applications and explore potential solutions. In summary, these three leading edge MLS DE design patterns present a robust set of solutions to the growing challenge of collaborating and working effectively in the ever-complex SAP community. By leveraging this capability, organizations can bolster security, consolidate costly licenses across networks and safeguard their most valuable data while also dramatically improving user operational efficiency on their primary network. By employing containerized applications, data transfers between networks are eliminated, reducing the risk of information leakage through unauthorized channels. Operational Technology (OT) plays a crucial role in controlling industrial processes and our critical infrastructure. However, with the rise of the Internet of Things (IoT) and increased connectivity, OT systems face amplified cyber risks. Historically isolated, these systems now often intersect with IT networks, making them vulnerable to threats, especially given their outdated software and the difficulty in patching them. The stakes are high: cyber-attacks on OT can disrupt power grids, halt manufacturing, and pose significant safety threats. Addressing these concerns requires a holistic strategy, integrating both OT and IT cybersecurity measures. As we advance in this digital age, it’s imperative that we prioritize and invest in the protection of these vital systems. In the presentation “Breaking Barriers with Generative AI: Enhancing Systems Security and Data Sharing for the Warfighter,” we will explore the transformative potential of Generative AI in the context of emerging technologies to support the warfighter. This presentation directly addresses the theme of the conference, which focuses on the intersection of systems security, access management, and data sharing. The Department of Defense (DoD) should care about the application of Generative AI because it offers a unique opportunity to overcome existing barriers and enhance the DoD’s systems security and data sharing capabilities. Generative AI has the power to revolutionize the way the DoD operates by enabling the creation of synthetic data, generating realistic scenarios, and simulating complex environments. This technology can significantly improve training, testing, and decision-making processes, leading to more effective and efficient warfighter operations. By leveraging Generative AI, the DoD can enhance systems security by simulating and identifying potential vulnerabilities, predicting and countering cyber threats, and developing robust defense mechanisms. Additionally, Generative AI enables secure and controlled data sharing, allowing the DoD to collaborate with partners, share information across agencies, and leverage collective intelligence while maintaining data privacy and integrity. The impact of embracing Generative AI in the DoD environment is significant. It empowers the warfighter with advanced tools and capabilities, enabling them to make informed decisions, respond rapidly to evolving threats, and achieve mission success. By breaking barriers with Generative AI, the DoD can enhance its operational effectiveness, improve situational awareness, and ultimately ensure the safety and security of the nation. Leveraging AI to augment our information forces gives us massive new capabilities. Adversaries know the same thing and are trying to do the same thing. A small amount of high-performance computing (HPC) in the right places will solve many problems of AI relating to deployment, engagement, and data ingestion in environments where data security and access controls are paramount. Using AI in secure, reliable, resilient, rapidly updated ways will give us an edge. Relying on commercial cloud providers for all computing, R&D, and services for machine intelligence is a risky way to get that edge. Relying on commercial cloud for the foundations and using in-house HPC expertise and resources to deliver the last mile of machine intelligence will reduce risk and accelerate the adoption of secure, reliable, robust, and repeatable AI inside the enterprise. Today’s warfighter is more connected than ever before to a streaming vector of actionable intelligence. Platforms, systems, and data – all traversing an ever-increasing number of endpoints. As we look to events around the world as leading examples of how the battleground continues to change, we are called to action to improve both the offensive and defensive digital capabilities of our military. To win, our priorities must clearly align to automating heterogeneous environments at a moment’s notice, delivering consolidated AI-infused digital experiences to each warfighter, and leverage Automation and AI to protect our digital advantage. Scaling quantum computers will eventually break the digital security used in virtually all modern data networks. For decades, our adversaries have been collecting encrypted communications with the intention of decrypting and operationalizing it when larger quantum computers become available. This Cold War technique is known as “harvest now, decrypt later” (HNDL); it makes headlines today because quantum computers can break our existing algorithms by brute force. The transition to Post Quantum Cryptography (PQC) does not solve the HNDL problem because the new algorithms have no mathematical proof of hardness. As such, NIST advised developers to be “crypto-agile” and prepared to replace PQC at any time in the future. For decades, implementation errors, weak encryption keys, poor randomness, corrupted software libraries and a variety of attacks resulted in the total exploitation of stored HNDL data. The issue is fundamental to the single-points-of-failure in public key infrastructure (PKI) which is based on a 1970s architecture predating the internet, cloud, virtualization, and containerization used in modern information systems. Qrypt leverages multiple quantum entropy hardware sources and distributed software algorithms to enable end-to-end-encryption (EE2E) with simultaneous key generation at any endpoint. This mechanism decouples the data from the decryption keys, eliminates key distribution and is unaffected by multiple weaknesses in the system, including the potential failure of the PQC algorithms and insider threats. The modern warfighter will operate in converged PKI environments on 5G/6G networks, using autonomous systems, in smart cities, built on technology under adversarial control. Secure communications will need much higher levels of assurance than currently possible. Incremental improvements to classical techniques will be insufficient in the quantum era. Kelly Dalton, AFRL Jonathan Thompson, AFRL This is an update to last year’s presentation regarding an effort to provide DoD funded, shared supercomputing to the acquisition engineering, research, development, and test & evaluation communities. Large scale supercomputers are funded by the DoD High Performance Computing Modernization Program for the purpose of providing no-cost computing to scientists and engineers working on DoD problems. Contractors can also access these resources under a DoD contract involving an RDT&E project. This unclassified/CUI presentation will provide information regarding current status and future plans by the Department of Defense to provide continued access to free supercomputing resources to government and contractors supporting special programs and/or SCI-related projects in the research, development, acquisition, and test & evaluation mission areas. Specifically, the large-scale computing resources provided by the DoD High Performance Computing Modernization Program (HPCMP) will be discussed as well as how to access these resources. The supercomputing systems undergo a recurring technical refresh funded by the DoD HPCMP. The individuals/organizations do not pay for compute time or storage on the DoD supercomputers as these are funded through the DoD HPC Modernization Program. USG has prohibited acquisition of hardware from sanctioned entities and excluded those companies from doing business in the United States. But most program managers don’t realize that those same sanctioned entities and foreign adversaries actively develop, maintain and control software dependencies used by classified military programs. While these dependencies can theoretically be code-reviewed before approval, they’re almost never reviewed beyond a one-time check for viruses or known vulnerabilities – with little to no monitoring of upstream risks. And even if their source code is reviewed, there’s no chain of trust between repositories and published packages. This talk will illustrate how Chinese and Russian developers are positioning in the upstream software supply chain, how that risk can be detected and how it can be managed in an automated way, at scale, in the absence of any known or detectable vulnerabilities in the code. Single-maintainer projects belonging to Russian government employees have been identified in federal APIs that handle highly sensitive data at high scale. The ecosystems in which adversarial entities are active include AI/ML used in defense, which was the subject of a year-long analytic project that Ion Channel (recently acquired by Exiger) executed for DTRA. The data backplane for identifying adversarial FOCI in upstream software dependencies has both defensive and offensive value in software-intensive programs and missions. Kathleen Featheringham, Maximus Michael Sieber, Maximus Frank Reyes, Maximus As the Defense Department (DoD) continues its cloud modernization journey with the Joint Warfighting Cloud Capability (JWCC) and other programs, managing sensitive data in the cloud is a top priority and cybersecurity challenge. Emerging technologies such as artificial intelligence (AI) offer novel strategies to fortify cryptographic practices, enhance data encryption, and bolster cloud security. Reaping the benefits of AI-powered cloud security requires good data practices and data governance as well as proper configuration management and modern encryption strategies to ensure data security. This session aims to address common cloud security concerns and outline use cases for comprehensive cybersecurity and encryption practices powered by AI to properly manage sensitive data in the cloud. Rob Case, DON SAP CISO An examination of the Risk Management Framework as a dynamic cybersecurity program featuring Cyber Hygiene, Cyber Readiness, and Continuous Compliance as prime disciplines. The end in mind is to finally mature beyond checklists and firefighting, develop locally relevant threat intelligence programs, prepare for continuous ATOs with fully developed ConMon programs, and generate feedback loops between the monitors and responders. This presentation explores the Risk Management Framework and JSIG control families as features of Cyber Hygiene (management of the authorized) and Cyber Readiness (management of the unauthorized) and encourages RMF practitioners to go beyond the ATO. The concept of outprocessing the checklist is encapsulated in a change of mindset; completing a task is not compliance and compliance is not security. Narrative-based bodies of evidence authored and informed by ISSOs are insufficient. Cybersecurity practitioners must seek system-based artifacts as their proof of configuration and ISSOs must be informed by the system. Chad Steed, ORNL Visual analytics is a viable approach for enabling human-machine collaboration in today’s most challenging data analysis scenarios. While the increasing volume and complexity of modern data sets severely limits the viability of purely manual, human-centered strategies, most data analysis tasks are inherently exploratory (meaning the user doesn’t know all the questions they may ask of the data beforehand) and require interactive query capabilities. Visual analytics solutions that balance human and machine strengths are ideal, but achieving such a balance is not trivial. It requires judicious orchestration of human strengths, namely creativity, intuition, visual perception, and cognition, with the computational power of machines and the automated algorithms that run on them. In this talk, I will discuss modern data analysis challenges and how visual analytics tools can help solve them. To illustrate these ideas, several visual analytics systems will be described with an emphasis on the integration of human interaction, data visualization, and algorithmic guidance into flexible tools. I will also highlight the application of these tools to real-world applications in explainable AI, sensitivity analysis, multivariate analysis, and text mining. I will conclude with an overview of active and future visual analytics work. Caleb Snow, WWT Kimberly Haines, WWT AIDN leverages state-of-the-art machine learning and artificial intelligence algorithms to detect and respond to even the most advanced and elusive threats. It identifies malicious activities in real-time, minimizing the potential impact of attacks. Through continuous monitoring through AIDN, your organization enjoys 24/7 monitoring of your digital infrastructure. AIDN provides immediate alerts and proactive threat remediation to prevent breaches before they occur. Through our User-Friendly Interface, our intuitive, user-friendly dashboard simplifies the complexities of cybersecurity management. It offers real-time insights into your network’s security posture, allowing for informed decision-making. AIDN is designed to grow with your organization. AIDNs threat intelligence integrates threat intelligence feeds from multiple sources, ensuring you stay ahead of emerging threats. This knowledge helps AIDN adapt its defenses and protect your organization from new attack vectors. Kenny Bowen, Microsoft Rebeka Melber, Microsoft Historically, the DoD SAP Community has faced a glaring challenge – one of disconnection. Over the past decade, a remarkable transformation has taken place. Thanks to a roll out of enterprise-level SAP capabilities over the past decade, connectivity has surged to unprecedented levels. These advancements have become the backbone of an entirely new era, opening doors to a consolidated stream of data that is poised to reshape the landscape of national defense. The proliferation of Cloud Service Providers (CSPs) authorized for SAP data further signals the dawning of this transformative era. In the midst of this technological evolution, it’s crucial not to overlook the basics. While the buzzwords of Artificial Intelligence and Machine Learning are reshaping our technological landscape, the foundation for these innovations must be steadfastly established. Our success hinges on getting the fundamentals right, ensuring that the most fundamental functions are in place. Collaboration emerges as the cornerstone that will pave the way towards a truly robust and effective national defense strategy. This talk will delve into the narratives of the past, the dynamic landscape of the present, and the exciting potential of the future. It encompasses communication between Defense Industrial Base (DIB) and Government, Enterprise and Mission Users, and General and Privileged Users. As we stand on the precipice of unparalleled technological advancements, it is our responsibility to steer this transformation with clarity, unity, and a shared vision. Through collaboration and convergence, we shall not only bridge past disconnects but also construct a foundation for a stronger, safer, and more technologically empowered future. John Loucaides, Eclypsium Not a month goes by without another deep vulnerability in CPUs, memory, BIOS, BMCs, or some other component buried inside nearly every piece of IT equipment. While these issues sound serious, the very premise of these components is to abstract away hardware details. With adversaries known to be exploiting these bugs, how can we assess vulnerabilities not mitigated by traditional endpoint security solutions? In this talk, John will explore some of the technical issues related to cyber security of the supply chain. He will explain the most common issues, how to check for them, and how to avoid being taken by surprise. Having personally been involved in research into and coordinated disclosure of serious platform-level vulnerabilities, John will speak from personal experience (both within USG and outside) to suggest practical solutions involving both open source and commercial tools to help with this evolving problem. After discussing issues that affect firmware updates, end of life, component vulnerability scanning, integrity checks, and sanitization/destruction, attendees will discover that even though perfection is impossible, all is not lost. Andrew “AJ” Forysiak, Varonis Chad Mason, Varonis The United States faces persistent and increasingly sophisticated malicious cyber campaigns that threaten the public, private sector, and ultimately, the American people’s security and privacy. By implementing Zero Trust (ZT) across all agency systems, the U.S. government seeks to protect high-value assets, but without first building a solid foundation, any zero-trust architecture will be largely ineffective and unwieldy. Agencies must now strive to provide best-in-class zero trust-based security while satisfying compliance requirements such as EO 14028, DOE O 471.1, and OMB 22-09. Zero Trust represents a paradigm shift in how we think about protecting our assets and requires a multi-phased process to deploy successfully. David Metcalf, UCF AI, Blockchain, and Cybersecurity (ABC) advances are reshaping the enterprise solutions that support the warfighter. This session provides a survey to explore use cases under development at University of Central Florida’s Institute for Simulation and Training including the ARO sponsored Blockchain and Quantum Defense Simulator for multi-protocol prototyping, modeling, and testing, Army TRACRChain Blockchain for automated range data from TRACR2, and Navy Project AI Avenger analysis of AI media scrubbing tools. A review of design, standards, early results, and scalability opportunities and issues will be shared. Synergy with other projects and next steps in ABC solutions to meet emerging requirements for cross-warfighter solutions will be presented. Tangible examples include a digital twin prototype to combine operational readiness and trusted career-spanning data from recruit to retire and a quantum computing cyber awareness and AI Assurance simulation platform. Using platforms like digital twins, quantum-as-a-service, large language models-as-a-service, and advanced simulations allow Commands to explore specialized use case, protocols, standards, and scalability before committing vital resources – leveraging modeling, simulation and analysis techniques such as NSF ICorps and Hacking for Defense. Concluding remarks include discussion of methods of collaboration between military, industry, and academia to leverage public university research and other nonprofit entities. Caden Bradbury, NetApp AI models are only effective if they can be utilized in the most extreme tactic edge scenarios. (Think: in the back of a Humvee, on a Naval Ship, in a remotely operated drone, etc.) While the training of accurate models is vital, the biggest challenge in these edge environments is moving data and models to and from the tactical edge to core data center. Models must be continuously improved to be used effectively. They must perform at the highest level possible for the DoD. This is especially true in life-or-death scenarios, like automated target acquisition models. To optimize models, new data must be continuously fed to the algorithm. In order to face the challenges posed by great power competition in the digital age, the Defense Intelligence Enterprise (DIE) must adapt its mindset and approach by embracing digital transformation. The DIE must accelerate digital transformation efforts to efficiently and effectively share data, information, and intelligence among Military Services, Defense Agencies, and Combatant Commands. A critical enabler of digital transformation is a seamless digital foundation. The Digital Foundation includes the services comprising the digital substructure that enables rapid deployment, scaling, testing, and optimization of intelligence software as an enduring capability. A digital foundation will achieve a simplified, synchronized, and integrated multi-cloud environment that can adopt innovation at scale and promote good cloud hygiene. The delivery of a Digital Foundation ensures DIE data, architecture, and infrastructure are integrated and ready to enable: Joint Warfighting Concepts, Innovation at Scale, AI, Augmentation, and Automation, and Zero Trust. As the agency has begun its journey to transitioning to Zero Trust, we have been meeting with industry partners to discuss best practices in order to support the objectives identified in National Security Memorandum 8; Improving the Cybersecurity of National Security, Department of Defense, and Intelligence Community Systems. We have initiated a prototype effort exploring innovation opportunities in order to enhance core service offerings contributing to the Zero Trust journey. This brief will highlight areas we are collaborating with community and industry partners to adapt our environments to be positioned for supporting future mission requirements with a secure data-centric enterprise. The ability to access data necessary to make battlefield decisions at the speed of relevance is critical to the Nation’s defense and tactical advantage. The Common Data Fabric (CDF) fast data broker is an evolution in data sharing across silos, organizational and mission boundaries making data available to any consumer machine that can enforce data policy. The CDF is a cloud-based commercial software data brokering capability that functions anywhere a connection can be established and easily integrates with existing and legacy architectures to make data available to U.S., Joint Taskforce Warfighters, US Allies and Mission Partners. CDF is deployed by the Defense Intelligence Agency (DIA) and is a foundational pillar of the data sharing vision of the Secretary of Defense as we transform the digital ecosystem towards an Enterprise Construct. CIO has applied Service Delivery Modernization to improve the customer experience. We have implemented large efforts to stand up In Person Service Centers, integrated Live Chat on the desktop, ensured our Knowledge Articles and IT Equipment Catalog are 508 compliant, Service Central automated workflows, @CIOTechTips, and small initiatives to improve IT training/lab sessions for our new officers, and play jazz music for our listeners as they wait for a technician to answer their questions. This presentation will be an opportunity to share the advanced services that have been implemented, share our journey map, and to hear from our customers in a question and answer session about what improvements they would like to see. We’ll introduce the 13 December 2021 Executive Order on Transforming Federal Customer Experience and Service to Rebuild Trust in the Government and time permitting, explore self-help options that are available (Self-service-password-reset, go words, cross domain dialing, extension mobility, virtual desktops, etc.) In 2018, there were more than 31,000 cybersecurity incidents affecting government agencies. In 2019, the U.S. government accounted for 5.6 percent of data breaches and 2.1 percent of all exposed records. It is imperative the US Government secures citizens’ information and federal agencies must continue to deliver services, regardless of cyber-attacks seeking disruption of those services. Fortunately, significant strides have been made to ensure just that. The Biden Administration’s budget request includes roughly $10.9 billion for civilian cybersecurity-related activities, which represents an 11% increase compared to 2022. To date, over a billion dollars has been awarded through NITAAC for cybersecurity solutions including training and awareness programs, professional and technical support services, and IT modernization for the Department of Defense, Department of Veterans Affairs, Department of Agriculture, Department of Justice, and more. In fact, all aspects of cybersecurity products, services, and commoditized services are readily available under the three Best in Class GWACs that NITAAC administers: CIO-SP3, CIO-SP3 Small Business, and CIO-CS. NITAAC’s federal customers can quickly obtain cybersecurity solutions without the tedious processes under FAR Part 15; instead using FAR Part 16.5 to issue task and delivery orders quickly and easily for mission requirements. Customers also have access to NITAAC’s secure electronic government ordering system (e-GOS) to further streamline competition, management, and award. During this session, NITAAC Deputy Director Ricky Clark will provide an overview of the NITAAC GWACs and discuss how as the U.S. government continues to roll out mandatory cybersecurity standards for government agencies, NITAAC can help agency partners raise the bar for cybersecurity beyond the first line of defense. The DIA Platform-as-a-Service (DPaaS) is an enterprise container management platform enabling application developers to build to a single standard that provides advanced and commonly used technical enterprise services necessary to decrease development time while achieving strategic competition goals. DPaaS enhances a developer’s ability to focus on functionality, enabling mission applications to be rapidly prototyped and move at the speed of mission by reducing technical overhead. This functionality coupled with DevSecOps and the Capability Delivery Pipeline (CDP) enables applications to be developed and deployed securely, quickly, and easily no matter the location or infrastructure, freeing up development teams from tedious and complicated deployments. The DoD and the US more generally is increasingly dependent on commercial products that provide crucial elements of our cybersecurity. Located in NSA’s Cybersecurity Collaboration Center (CCC), Standards and Certifications plays a significant role in shaping the marketplace for these products across the lifecycle of development. Through its leadership in standards bodies (ensuring that critical security requirements are built into the standards that commercial products implement) and its leadership of the National Information Assurance Partnership (which sets the testing requirements for commercial products that will protect classified information and systems), Standards and Certifications establishes a baseline that products will be built to and tested against. The placement of Standards and Certifications in the CCC enables it to bring to bear relationships with Defense Industrial Base companies as well as NSA’s enormous capacity for threat intelligence to inform and strengthen the standards and certifications mission. This talk will provide the audience with an overview of NSA’s standards and certifications programs, give examples of how the programs raise the level of security in commercial products that protect DoD systems and describe how our DoD customers can help us by providing concrete requirements that strengthen our bargaining position in standards development organizations. Develop Network Infrastructure More Rapidly, and Operate It More Securely and Effectively. Using model-driven DevOps and the Infrastructure as Code (IaC) paradigm, teams can develop and operate network infrastructure more quickly, consistently, and securely–growing agility, getting to market sooner, and delivering more value. This is a pragmatic talk about implementing model-driven DevOps for infrastructure. It contains insight in to lessons learned and illuminates key differences between DevOps for infrastructure and conventional application-based DevOps. Whether you are a network or cybersecurity engineer, architect, manager, or leader, this talk will help you suffuse all your network operations with greater efficiency, security, responsiveness, and resilience. This session will describe how to leverage graph database technology to enhance analysts’ ability to fuse together and interact with extensive volumes of data from disparate intelligence feeds, both controlled/protected and publicly available/open source. These disruptive graph-based views can be integrated into most existing analysis platforms, extending and providing more immersive views and experiences with data and the ability to extract meaningful and actionable insights as data volumes increase in size and complexity. Through these new graph database views, analysts interact with data represented by nodes and edges. This flexible data architecture allows for rapid filtering of data layers, producing a truly immersive environment filled with color, highlighting, line thickness, borders, icons, badges, and more, allowing the analyst to fully leverage graph database node and edge methodology. These visual cues help the analyst to find and link critical pieces of data together, providing highly reliable information that the analyst uses to see data more clearly, make more accurate predictions, and be confident in their decision-making. Join the DIA Chief Information Officer, Mr. Doug Cossa, as he moderates a discussion on the future of CIO considering the ever-evolving landscape of Information Technology. Panel members will feature junior civilian personnel across DIA CIO—the forces on the ground implementing DIA CIO’s key initiatives and riding the waves of the latest technological advancements. Through this session, attendees will gain a better understanding of DIA CIO’s current successes and challenges from the action officer viewpoint. Further, attendees will gain insight into how the Intelligence Community and Department of Defense must continue to evolve to enable mission. The IC treats data and software as strategic assets. The IC transcends strategic competitors through innovation, adaptation, and collaboration by facilitating a shared environment for software modernization. We set the foundation for success via common software environments, which provides a mature, versatile DevSecOps environment for internal and external teams. This game-changing tool suite and associated approach provides the fastest way to deliver mission-specific software — independent of the underlying data and infrastructure. It enables teams to have quick delivery to operations, security early on, and the benefit of code sharing and reuse. This presentation will provide an overview of that ecosystem and will focus on how: Internal and external DoD and IC teams are provided with: Industry leading DevSecOps capabilities. Parity of tools on all security domains. Low to high automated movement of code and artifacts. Maturity of capabilities How to onboard: Completion of the external team questionnaire hosted on Intelink at https://go.intelink.gov/ku5ZQH2 Coordination of a service agreement and funding. Challenges Reciprocity People/Process The ongoing strategic power competition along with the adversarial implementation of innovative technology, such as Artificial intelligence (AI), has emphasized the need for increased awareness and strategic warning in nearly every warfighting domain. Increased use of this technology provides a unique challenge and strategic avenue for the U.S. Intelligence Community and its partners as they seek to maintain their competitive edge in the era of near-peer adversary competition. This research project addresses Edge AI technology affecting the U.S. strategic defense posture in the Space Domain. The use of this dynamic technology in one of the most influential and uncharted mission spaces lends an insightful discussion on the cascading effects of AI advancement. This project has the potential to lend itself to further engagement with the private sector, as well as future substantive research projects. To address our methodology, we will divide this research into a discussion on the existing technologies that would be impacted given the event of a flash war in space. Discussion on the interconnectivity and vulnerabilities of these systems, the way Edge AI would be able to potentially augment or damage the intended functionality of these systems, as well as the legal ramifications for the use of edge AI in the space domain. It would include communications satellites, GEOINT constellations, ground nodes, and cloud data storage. It is important to note that though policy capabilities and funding specific to each military branch are important considerations regarding AI employment within the Joint All Domain Command and Control (JADC2) architecture, expanding these topics in detail would extend beyond the scope of our project. For a number of years, strategic competitors have exploited and subverted vulnerabilities in the DoD/IC supply chain. These adversarial efforts, which includes stealing U.S. intellectual property, results in decreased confidence in securing critical solutions, services and products delivered to the DoD. Contractor facilities supporting hardware/software design, development, and integration are frequently targeted as cyber pathways to access, steal, alter, or destroy system functionality. Since malfeasant activities can compromise government programs or fielded systems, DIA continues to evaluate and implement efforts to harden its supply chain commensurate with the risk to national security. Within DIA’s implementation of the Risk Management Framework, DIA has aligned cyber supply chain risk management with the acquisition process and engineering strategies. These efforts enable DIA to create a framework for cybersecurity due diligence – influencing the Intelligence Advantage. This session will describe and clarify DIA’s implementation of the DoD/IC supply chain risk management program. Specifically, the briefers will discuss how Cyber supply chain risk management has been integrated within cybersecurity, engineering, and DIA’s acquisition strategy. Both internal and external customers will also obtain knowledge of: (1) How to obtain DIA’s SCRM Services, and (2) best practices to actively and pre-emptively address supply chain threats. While detailed information would normally be provided on a need-to-know basis at classified levels, our session will not cover any details that would expose classified information. Since this conference is unclassified, we are only going to speak to large trends, concepts, and generic activities. There will not be any details provided to attendees about any particular agency’s status, and we will not be discussing vulnerabilities that could be exploited by adversaries. Service mesh can play an important role providing a zero-trust networking foundation, however, it also poses a few operational and security challenges. First, in current implementations, a service mesh is opt-in by deploying a sidecar process with the secured resource. Second, tying infrastructure components into application deployments makes it more difficult to patch and upgrade when vulnerabilities are discovered. Lastly, current service mesh implementations can be difficult to extend to existing workloads. In this talk, we dive into an “ambient” service mesh that runs without sidecars and addresses these previous issues without trading off zero-trust properties. The Public Sector must deliver on ever-expanding missions while battling against siloed legacy applications and vast, untold volumes of information. This session will explore how Defense Logistics Agency, a 26,000-person combat support agency for the U.S. Department of Defense, has treated AI-powered content management as a strategic tool to save time and energy to supply the warfighter. Learn how DLA has gained an information advantage in supplying the U.S. military with its equipment needs. Topics covered will include military moves, supply chain and audit readiness, content services, intelligent capture, password complexities, and unstructured content. Enabling classified communications and situational awareness can be difficult and expensive for deployed, remote, collaborative, and contingency use cases. Following guidelines from NSA’s Commercial Solutions for Classified (CSfC) program can overcome many challenges associated with legacy systems for classified communications and can help organizations benefit from the fast pace of commercial innovation in mobile devices. Using CSfC, organizations have options for enabling executive mobility and remote work (e.g., using laptops and smartphones), site-to-site extensions of classified networks (e.g., for remote tactical teams, branch offices, home offices, or multi-building campuses), and classified campus-area Wi-Fi networks. This session covers how to design and deploy systems conformant to the CSfC program and illustrates specific real-world examples of systems in use today for federal enterprise and tactical use cases. This session also covers emerging technologies and solutions that address the newly updated CSfC requirements such as continuous monitoring, as well as complexity challenges inherent in these solutions. The session will provide insight into the Intelligence Community’s IT and mission needs. Industry attendees will learn how to utilize the Joint Architecture Reference Model (JARM) to address requirements on IC elements acquisitions. IC attendees will learn how to align priorities into mission resource needs across the Doctrine, Organization, Training, Materiel, Leadership and Education, Personnel, Facilities, and Policy (DOTMLPF-P) moving down from their strategy to define capabilities and their enabling technical services. The session will demonstrate how the JARM can be utilized to make invest/divest decisions, develop IC Service Provider catalogs, and discover IC services. JARM supported capability gap analysis will also be demonstrated by using heat maps to align investment to capability and service needs. DoD attendees will learn how to define their architecture to integrate with the IC. The US Army National Ground Intelligence Center (NGIC) is exercising a portfolio-based approach to transition its mission capabilities to the cloud through rationalization, integration, and modernization. A key strategic focus is human capital and talent management that holistically invests in its workforce shifting from declining IT responsibilities to focus on emerging skills and disciplines such as cloud computing, data engineering and modern application development. This briefing will describe the human capital and talent management strategy and implementation plan to drive operational readiness of its IT workforce to meet the current and future demands of the NGIC mission. This will also include a demonstration of the tooling used to visualize the IT workforce’s skills and disciplines mapped to mission needs and capacity. The space domain requires analysis in four dimensions (x, y, z, t). Unlike the other warfighting domains, space planning, wargaming, and decision making must be done using tools capable of multi-dimensional visualization and simulation of near-Earth orbits (e.g., Analytical Graphics/Ansys Incorporated Systems Tool Kit, or STK). Such tools have proliferated over the last decade across a vast array of government and non-government space users. Much like the Microsoft Office 365 suite of productivity tools, or Adobe’s Acrobat/Creative Suite, Systems Tool Kit has become the modeling and simulation software of choice for those involved in the national security space arena. In the area of orbital warfare training specifically, STK is used an instructional aid to make tangible the realities of space flight, systems engineering, astrodynamics, and orbit propagation. Organizations like the US Space Force’s National Security Space Institute, US Space Command, rely on STK to perform computations and analyses to inform real-world decision making during critical moments of space launch, orbit maneuver determination, and other activities in space. In this regard, modeling and simulation technologies for the space domain have become as ubiquitous as Microsoft-type productivity software deployed on a standard desktop configuration. Therefore, STK or other software tools like it, must be treated as a productivity tool and not as a special-use case to be found in a high-performance computational center or battle lab. Licensing arrangements, deployable efficiency, and proliferation must continue to be made advantageous to the average space user. As part of NGA’s greater multi-tier Edge Strategy, the JREN is being deployed to Combatant Commands. This highly scalable capability is designed to position significant storage, compute, transport bandwidth, and applications closer to the Tactical edge. JREN will support expanding Department of Defense, Intelligence Community and Coalition customer requirements with content specific to their area of operations, GEOINT/partner applications and high-performance compute. Design considerations include: increased resiliency in Denied, Degraded, Intermittent, Limited (DDIL) communications environments via direct satellite downlink, reduced transport latency, and the use of the NGA CORE software development method to develop, deploy, and sustain modern GEOINT software. All designed to facilitate the movement of critical intelligence and data sharing. Deployment has started at USINDOPACOM with additional COCOMs receiving delivery in the upcoming outyears. With more than 15% of the world’s population experiencing some form of disability, DIA understands accessibility is more than an adherence to Section 508 standards. It’s about inclusive design – developing digital solutions to meet a broad spectrum of intersectional needs, perspectives, and behaviors, rather than solely creating accommodations for specific disabilities. This presentation will describe resourceful ways DIA is expanding its IT accessibility expertise across the Enterprise and how to utilize collaborations with Industry to develop innovative solutions like a speech recognition application for its Deaf and Hard of Hearing community. This presentation will share DIA’s plan to integrate accessibility and inclusivity into its software development lifecycle rather than adding it on as an afterthought. The IC Security Coordination Center (SCC) is the Federal Cybersecurity Center for the IC and coordinates the integrated defense of the IC Information Environment (IC IE) with IC elements, DoD, and other U.S. Government departments and agencies. Working with the other defense-oriented Federal Cyber Centers—the Joint Force Headquarters (JFHQ) Department of Defense Intranet Information Network (DoDIIN) and the Cybersecurity and Infrastructure Security Agency (CISA)—the IC SCC facilitates accelerated detection and mitigation of security threats and vulnerabilities across the IC by providing situational awareness and incident case management within the shared IT environment. In FY ’23 the IC SCC is enabling a better IC cyber defense posture through the procurement of IC-wide enterprise licenses of commercial Cyber Threat Intelligence from multiple vendors, an Endpoint Detection and Response (EDR) pilot program for IC-wide adoption, and an enhanced patch repository for prioritizing patch management and driving down shared risk across the enterprise. Join us as we detail these initiatives and how they can help secure your environment! The session will provide an opportunity to hear from Chief Architects from NRO, NSA, NGA, DHS Coast Guard, DNI, and DoD. The panel will be hosted by the Intelligence Community Chief Information Office (IC CIO), Architecture and Integration Group (AIG). The panelists will respond to questions on how they are shaping their agency’s technology roadmap and how they coordinate and drive mission integration within their element and across the IC and DoD. Attendees will gain understanding of programs and initiatives across the IC that are modernizing systems that support the intelligence lifecycle and improve integration. The panel will leave the attendees with a better understanding of the role of the Chief Architect within each represented organization. At DoDIIS 2021, the Army Military Intelligence (MI) Cloud Computing Service Provider (AC2SP) briefed the mission outcomes realized by leveraging its cloud-based Data Science Environment (DSE) to rapidly respond to a mission requirement in less than two weeks from problem to solution. This briefing will build upon the prior successes and describe the AC2SP Data Science Product Line to include its core product offerings and underlying cloud services supporting Artificial Intelligence and Machine Learning (AIML) to enable multi-tenancy and respond to the variability in data science requirements across the Army Intelligence and Security Enterprise and multiple operational networks. We hear that promoting and maintaining a healthy work environment is important. Cyber and physical security threats from trusted insiders are on the rise and there is evidence that what happens in the workplace impacts motivation for and mitigation of possible attacks. This interactive presentation introduces research and case studies to highlight the complex role the work environment and the resulting work culture play in deterring and mitigating risks that can lead to attacks that harm national security and result in loss or degradation of vital resources and capabilities. The presentation includes promising practices for those who want to improve their respective work environments and reminders for those already doing the work. The topic offers an opportunity to engage, reflect and specific examples of ways to innovate, adapt and collaborate to improve and protect work settings that are increasingly targeted by our adversaries. Technological innovation is disrupting societies with serious implications for the era of Strategic Competition. AI is rapidly emerging as a powerful technology with the ability to illuminate tactical and strategic advantages against our competitors. Federal mandates, such as the National Security Commission on Artificial Intelligence’s mandate that all Intelligence Community (IC) and Department of Defense (DoD) entities be AI-Ready by 2025, reinforce the urgency and imperative of leveraging AI. In response to this mandate, DIA’s Chief Technology Office (CTO) was named as the office of primary responsibility for DIA Strategy Line of Effort (LOE) 2.9 – AI Readiness, outlining how the Agency can reach AI readiness, AI competitiveness, and AI maturity. The purpose of LOE 2.9 Is to transform culture and capabilities, creating an AI ready workforce that enables DIA officers and organizations to innovate, incorporate and advance AI throughout Agency missions and processes to meet the demands of Strategic Competition and obtain data driven dominance. CTO is collaborating with partners across industry, academia, IC, DoD, and Five Eye (FVEY) to create a strategy that will ensure we meet this purpose. Learn about the DIA AI Strategy goals and objectives and the key pillars for transforming DIA into an AI Ready organization. The DIA Data Hub’s (DDH) objective is to offer an Agency data platform that ensures easy discovery of and secure, automated access to DIA data assets. The DDH concept will modernize the DIA’s data handling, storage, and delivery by using best-of-breed technology and treating data as an enterprise-wide asset. DDH will both provide a place for new data to reside, as well as free existing data from process and technologically driven silos. By treating data as an enterprise-wide asset, it will give mission and business analysts the full range of information necessary to provide insights to stakeholders ranging from the warfighter all the way to congress. DDH’s strategy is to meet customers where they are, enabling customers to keep data and services where they need it. This capability will allow data scientists to comingle data to derive new insights, and let developers quickly build applications by leveraging DDH as their data store. When data is treated as an asset, it opens the door to new efficiencies, insights, and capabilities. By providing all DIA users the data they need, DDH creates a foundational capability that will be key to maintaining a strategic and competitive advantage over our adversaries. The Transport Services Directorate Senior Technologist at the Defense Information Systems Agency (DISA) provides a strategic outlay of future enabling technologies, initiatives and capabilities that will deliver the next generation of global resilient communications capabilities to the warfighter. He will provide a strategic roadmap on the DISN core global transport evolution – from the barriers, to modernization areas, and information sharing approaches – to deliver a no-fail long haul transport architecture for DoD, Intelligence Community, US and Allied Government capabilities. Additional discussion on the need for joint mission integration to ensure the operational status of the underlying environments can be seamlessly integrated with the different domain owners, such as DIA, to assure end-to-end mission delivery and performance. Understanding Artificial Intelligence in IT Operations (AIOps) can be a daunting task given the various definitions of the term. IT Operations teams are seeking the advantages of Machine Learning (ML) and Artificial Intelligence (AI) to unlock better decision-making and to drive automation and self-healing to support mission essential applications. AIOps is not a single product, rather a journey where key components intersect and leverage machine intelligence and speed to drive outcomes. Join Lee Koepping from ScienceLogic as he de-constructs the essential elements of AIOps and how context driven observability and automated workflows can accelerate mission results to optimize IT service delivery. For years, operations squadrons across the globe used whiteboards and printed crew binders to execute global missions. A handful of aircrew members teamed up with Platform One to revolutionize the way crew management and distributed operations are done using a commercial-off-the-shelf (COTS) solution hardened and hosted on government servers. We discovered a fast and secure way to pass mission data from operations centers to crew members enhancing safety and mission velocity. This collaborative command and control flow enabled the early recognition of issues allowing us to maximize crew effectiveness on the road. The team used a Small Business Innovation Research (SBIR) grant to work with Mattermost to make defense enhancements focused on Air Operations Center workflows and needs. We realized that these types of collaborative capabilities allowed us to build a shared reality outside of our silos and solve issues before they occurred. This capability was demonstrated during the Kabul evac where stage managers took full advantage of the ability to self-organize and collaborate during the Kabul evac enabling the largest Noncombatant Evacuation Operations (NEO) in U.S. history. This talk gives an in-depth look at how innovation and technology laid the ground work for success. This talk will present an overview of DNS cyber attacks over the past several years by Advanced Persistent Threats (APTs) and how the types of attacks and mitigations have evolved over time. It will discuss why DNS continues to be a commonly used vector for adversaries and how cyber defenders can innovative to strategically defend against the most sophisticated APT using complex DNS techniques for malicious activity. As strategic competitors continue to adopt AI as a disruptive technology used to advance warfighting and intelligence gathering capabilities, it is imperative that the defense community come together to develop solutions for leveraging human-machine teaming to achieve decision advantage and dominate our strategic competitors. This panel will address how the Intelligence Community (IC) and Department of Defense (DoD) utilizes AI to continue to revolutionize the way we maintain strategic and tactical advantage in an era of Strategic Competition. Attendees will hear from AI experts spearheading efforts within their agencies to adopt AI as a means to outpace our strategic competitors and ultimately prevent and decisively win wars. Agencies include: the National Security Agency, the Central Intelligence Agency, and the Chief Digital and Artificial Intelligence Office. This panel will be moderated by DIA’s Chief Technology Officer and AI Champion. Many compliance officers inherit the negative reputation of, “wearing the black hat,” generating fear of involving them early and often in discussing current architecture, planning new infrastructure, or establishing programs. Strategic competition requires compliance officers and programs to participate early in the planning processes to streamline development and thereby ensure a reduction in incidents. Compliance officers must pursue opportunities to evolve their reputation and work with innovation leaders in a collaborative relationship that shifts outcomes to the benefit of the community, government, foreign partners, and taxpayers.
correct_foundationPlace_00033	FactBench	2	84	https://www.dataversity.net/enterprise-nosql-drives-synthesized-meaningful-data-next-gen-apps-processes/	en	MarkLogic's New Enterprise NoSQL Solution Drives Next	https://d3an9kf42ylj3p.c…z_ml9_052916.png	https://d3an9kf42ylj3p.c…z_ml9_052916.png	[ "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://dv-website.s3.amazonaws.com/uploads/2016/05/jz_ml9_052916.png", "https://dv-website.s3.amazonaws.com/uploads/2016/05/jz_ml9_052916.png", "https://d3an9kf42ylj3p.cloudfront.net/uploads/2024/01/1x1.png", "https://d3an9kf42ylj3p.cloudfront.net/uploads/2024/01/1x1.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Twitter_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Twitter_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Linkedin_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Linkedin_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Youtube_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Youtube_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Flipboard_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Flipboard_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Facebook_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Facebook_white.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.26-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.26-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.16-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.16-PM.png" ]	[]	[]	[ "" ]	null	[ "Jennifer Zaino" ]	2016-06-02T07:30:48+00:00	Enterprise NoSQL database provider MarkLogic has been pushing down that path by helping organizations integrate their data – structured and unstructured – in one place with its schema-agnostic data model for some time now. It’s possible to load data as is into the system and use a universal index to get at it.	en	/wp-content/uploads/2015/10/DV-R-1025-Transparent.png?x38402	DATAVERSITY	https://www.dataversity.net/enterprise-nosql-drives-synthesized-meaningful-data-next-gen-apps-processes/	It’s time for the enterprise to seize the opportunity to build new applications and processes based on a synthesis of meaningful data brought together from diverse systems. Doing so requires some key things, though, starting with a database platform that supports seamlessly integrating the data and ensuring that it is understandable at the conceptual level. Enterprise NoSQL database provider MarkLogic has been pushing down that path by helping organizations integrate their data – structured and unstructured – in one place with its schema-agnostic data model for some time now. It’s possible to load data as is into the system and use a universal index to get at it. “There’s been so much talk over the past years about Big Data. But in the enterprise space there’s often not even the opportunity to have Big Data, because data is broken into so many different systems, applications, and silos that they can’t bring it together,” says Joe Pasqua, MarkLogic EVP of Products. “We want to bring it together not just for data warehouse or analytics but to operate on.” The MarkLogic database also has long had in place a semantic foundation. It acts not only as a document store for storing JSON and XML, but offers an integrated triple store for storing RDF triples that can be linked together to describe facts and relationships. Now, MarkLogic is preparing to take things to the next level, with a platform for building next-generation applications with MarkLogic 9 that previewed in early May of 2016, and is due to ship by year’s end. The latest version, says Pasqua, is going to further drive the possibilities for a database to do smart things to help build next-gen applications, rather than just serve as a dumb repository of data. “If you’re not going to tell the database anything about the data, then there’s only a limited set of things the database has the opportunity to do,” he says. Building up Database Smarts MarkLogic 9 changes the equation, building on the semantic foundation it has in place to provide new capabilities such as Entity Services, which let developers give their data consistent meaning using a semantic model of the key concepts and the relationships between them. It’s a way to provide a high-level concept of business entities vs. a detailed low-level description at the physical level, and to let databases do something for developers that they didn’t have a chance to do before. “We store that information, version it, and make it available to apps in a consistent way, but also to the database to get smart about things,” he says. This can lead to automatically creating REST APIs for sharing customer entities, product entities, supplier entities, and so on. “It’s important because of the world of increasing micro-services,” he says, such complex applications are composed of small, independent processes communicating with each other via APIs. “You need an architecture that directly and natively supports that.” Another new feature is the Optic API query mechanism. As a document-oriented NoSQL database, it’s natural to query information as documents, Pasqua says, but sometimes developers want to see tabular data. “This lets you look through a tabular lens and see data in tabular form and do rollups and aggregates on it,” he says. Alternately, it lets users see data through a semantic lens or even see semantic data through a tabular lens. “It lets you have the most natural way of looking at data depending on what you are trying to achieve,” he says. Hidden from the developer are underlying technologies: A new index and distributed execution across a cluster for fast and effective performance. The SQL capabilities present in this enterprise NoSQL database are enhanced too, for integrating data from MarkLogic with existing SQL tools. “Folks have tools like Tableau that use ODBC [Open Database Connectivity] to get at data and we must provide a bridge so customers can use the tools they depend on and to get value out of MarkLogic at the same time,” he says. Enterprises are in a transition period, he notes, and there’s got to be a connection so that people can get their jobs done today but also move forward. “Our big challenge to ourselves is how to give them better tools to deal with what they have got, but also to allow them to move onto the next generation,” says Pasqua. Continuing Focus on Security The last thing a CIO wants to see on the front pages of newspapers and websites is a headline screaming that his or her company’s data has been breached, and that’s a big reason MarkLogic has always taken security seriously, as has its use for sensitive government applications, Pasqua says. In the security realm, it’s been distinguished as a Common Criteria-certified NoSQL database, for instance. Today, the market generally is looking to focus more tightly on security for the enterprise. For MarkLogic 9, that means a few things, starting with adding advanced encryption capabilities to deal with outsider and insider threats. Encryption technologies will reside in the core of the database, and even system administrators with root access to the system won’t be able to see encrypted data, for instance: “Advanced key management capabilities to keep things safe, along with fine-grained controls over what even administrators can do with the database from a security perspective, are very important to customers,” he says. Redaction features are part of the picture, too. “The idea is that part of the goal of bringing data from different systems is to make that data valuable to more people, so you want to give them access but you may need to redact certain elements of the data depending on who uses it,” he says. For example, a healthcare organization may want researchers to get their hands on data that can be highly valuable for researching disease treatments, but certainly they don’t want those researchers to have access to patients’ personally identifiable information (PII). With MarkLogic 9, the PII can either be removed or randomized. There are plenty of other scenarios where that capability has additional value in large enterprise environments, too. For instance the best testing for QA environments happens when the data used reflects what’s really going on in the production system. But of course businesses don’t want real, sensitive data just floating around in those environments. “Redaction lets you take the data out of production, redact it and put it right back into the QA environment,” he says. MarkLogic 9 also is adding to the role-based security it has incorporated at the document level by adding the same at the element level. So, an individual document can have elements in it that are top secret, for example, and others that are merely secret. “Depending on the clearance level of the people querying information, they will only see the information they are allowed to see in a single document,” he says. MarkLogic has an advantage in that it doesn’t have to bolt on security to its solution as some other enterprise NoSQL products might, since the company always has had an enterprise focus. Generally speaking, Pasqua says, NoSQL started out being used in places and for tasks where security was not a paramount issue. “The challenge is that once you build a system, it’s hard to go back and get security into it, versus building the fundamentals of it into it from the beginning,” he says. “Obviously it’s of paramount importance, though, and doing it right is the challenge.” Manageability Matters and So Does the Cloud Pasqua also points to manageability as a critical issue as more data comes together, systems get bigger and replication expands across geographies. So MarkLogic has created an Ops Director single pane of glass to view an organization’s entire MarkLogic infrastructure and manage it uniformly. A Rolling Upgrade feature was created in the service of non-disruptive operations, so that new versions can be installed on one machine in a cluster while the application stays running elsewhere, with the installation then rolling through the cluster, so that customers don’t have to experience downtime. The company also is mindful of the growing prominence of the Cloud in the enterprise: MarkLogic already is in the Amazon marketplace and runs on Microsoft Azure and Google Cloud. While some features in the latest release aren’t Cloud-specific, they are Cloud-relevant, he notes. For example, its encryption enhancements could help ease the concerns of customers about taking their data to the Cloud and having external service provider administrators supporting those systems, he says. Enhancements to MarkLogic 9’s tiered storage usage capabilities also “make it smarter about the way it uses storage tiers and how they are queried, and that makes it more effective for enterprises to use the cloud cost effectively,” he says. MarkLogic already has begun an early access program for MarkLogic 9, which it will be expanding. “We like to do that because it lets customers give us feedback while in the development process,” says Pasqua, “and it’s good for them because they can start building next-generation apps with new features now.”
correct_foundationPlace_00033	FactBench	1	45	https://www.cmswire.com/cms/enterprise-cms/marklogic-ceo-says-its-time-to-bring-marklogic-into-the-big-data-spotlight-010768.php	en	MarkLogic CEO Says Its Time to Bring MarkLogic into the Big Data Spotlight	https://www.cmswire.com/default-2.jpg	https://www.cmswire.com/default-2.jpg	[ "https://www.cmswire.com/api/fontawesome/fa-solid%20fa-book-open.svg", "https://www.cmswire.com/api/fontawesome/fa-solid%20fa-headphones.svg", "https://www.cmswire.com/api/fontawesome/fa-solid%20fa-binoculars.svg", "https://www.cmswire.com/api/fontawesome/fa-solid%20fa-users.svg", "https://www.cmswire.com/default-2.jpg", "https://www.cmswire.com/api/fontawesome/fa-solid%20fa-share.svg", "https://www.cmswire.com/api/fontawesome/fa-regular%20fa-bookmark.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-linkedin.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-linkedin.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-x-twitter.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-facebook.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-linkedin.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-x-twitter.svg", "https://www.cmswire.com/api/fontawesome/fa-brands%20fa-facebook.svg" ]	[]	[]	[ "" ]	null	[ "Barb Mosher Zinck" ]	2011-04-06T18:38:00+00:00	MarkLogic (news, site) has a new CEO and he thinks it's time to bring MarkLogic into the limelight as the go-to provider for information management.The Excitement of Unstructured Data Called HimKen Bado started at Autodesk in 2002.	en	/static/favicon.ico	CMSWire.com	https://www.cmswire.com/cms/enterprise-cms/marklogic-ceo-says-its-time-to-bring-marklogic-into-the-big-data-spotlight-010768.php	MarkLogic (news, site) has a new CEO and he thinks it's time to bring MarkLogic into the limelight as the go-to provider for information management. The Excitement of Unstructured Data Called Him Ken Bado started at Autodesk in 2002. For eight years he did sales and service for the company helping take it from revenues of US$ 800 million to US$ 2.3 billion. If that doesn't sound good enough, shares increased from US$ 6 to US$ 50 during his time. When he left, he was executive vice president of sales and service. When we talked to Bado, he told us that he had made the decision that if the right CEO position came along he would take it. And when he was on a sabbatical from Autodesk last summer, that opportunity presented itself when he was contacted by Sequoia regarding MarkLogic. Bado was looking for a real company with real work, one that was looking to scale out. The idea of working for a company focused on supporting the reams of unstructured data excited him. And MarkLogic is a growing company. Currently it has 250 employees and 240 customers, the majority who are using MarkLogic on mission critical systems. Learning Opportunities Taking MarkLogic into the Known World His challenge as he takes the reigns is that MarkLogic may have a a solid product, but it's really not that well known. According to Bado, people aren't sure where MarkLogic fits. And dealing with unstructured data -- and big data at that -- is a relatively new market. But big data is MarkLogic's sweet spot. Ron Avnur, Vice President, Engineering told us he believes what they are doing is unique in the market. The ability to put data into a real-time system and do real analytics/queries in seconds is definitely important to many of today's enterprises. MarkLogic supports some of the biggest data hogs (i.e. the little three letter government agencies) and has been a key enabler for many publishing organizations to move to the digital world. MarkLogic has the foundation, the technology, and the people to be a multi-billion dollar company. In a relatively short time, the company has experienced tremendous growth and now it is my job to multiply it ten times over,” said Bado Go To Market Approach Bado filled us in on MarkLogic's go to market approach. Its model has been around enterprise selling and has been very successful, but Bado said if it want to truly scale then it needs to leverage partners. There are also plans to take down barriers for developers who want to work with MarkLogic, and it is looking at how to support the student community. As for the technology roadmap, Avnur indicated that MarkLogic would be aggressive on the tools they develop.
correct_foundationPlace_00033	FactBench	1	7	https://www.4vservices.com/is-marklogic-the-right-database-for-me/	en	Is MarkLogic the right database for me?	https://www.4vservices.c…/footer-logo.png	https://www.4vservices.c…/footer-logo.png	[ "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/linkedin-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/seacrh-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/sms-ico.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/logo.png", "https://betaweb.wordsystech.com/schweizerrsg/wp-content/themes/schweizerrsg/images/search.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/searchcross.png", "https://www.4vservices.com/wp-content/uploads/2023/05/logo.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/inner-banner-bg.svg", "https://www.4vservices.com/wp-content/uploads/2023/11/ProgressMarkLogic_PrimaryLogo.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/blog/quote.svg", "https://www.4vservices.com/wp-content/uploads/2024/07/ProgressMarkLogic_PrimaryLogo_Stacked.png", "https://www.4vservices.com/wp-content/uploads/2023/07/started-working-at-MarkLogic-1.png", "https://www.4vservices.com/wp-content/uploads/2024/05/Reverse-Query.png", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/cta-bg.svg", "https://www.4vservices.com/wp-content/themes/4V-services/assets/images/logo.png" ]	[]	[]	[ "marklogic" ]	null	[]	null			https://www.4vservices.c…/footer-logo.png		https://www.4vservices.com/is-marklogic-the-right-database-for-me/	27 April, 2024 By Dave Cassel No Comments Progress MarkLogic is an enterprise, multi-model, NoSQL database, search engine, and application server. It's able to cover a lot of use cases, but there's only one that matters -- the one you're trying to implement. How do you know whether MarkLogic is the right fit? If you're considering MarkLogic, see the special offer at the bottom of this post. Use Cases Where MarkLogic Excels Semi-structured and Unstructured Data Management: MarkLogic thrives in scenarios where handling semi-structured or unstructured data is paramount. Its native support for XML, JSON, RDF, and binary content makes it an ideal choice for organizations dealing with combinations of diverse data types, such as documents, social media feeds, scalar data, and even ontologies. Complex Querying Requirements: With its powerful search and indexing capabilities, MarkLogic excels in use cases that demand complex querying and advanced search functionalities. Whether you're dealing with full-text search, faceted search, geospatial queries, or semantic-powered concept search, MarkLogic's flexible query engine can efficiently handle diverse search patterns. Enterprise Data Integration: For enterprises seeking a unified platform to integrate disparate data sources, MarkLogic offers robust data integration capabilities. Its built-in support for data ingestion, transformation, and harmonization simplifies the process of consolidating data from various sources into a coherent and accessible repository. Security is a Top Priority: MarkLogic boasts top-notch security features like built-in encryption at rest, role-based access control, and element-based security. Access control is part of MarkLogic's indexing strategy, removing the worry that a logic error may expose data improperly. This makes it a strong choice for sensitive data or applications in regulated industries. Mission-Critical Applications: MarkLogic's architecture, featuring built-in redundancy, scalability, and ACID compliance, makes it well-suited for mission-critical applications requiring high availability, reliability, and data consistency. Industries such as healthcare, finance, and government, where data integrity is paramount, often leverage MarkLogic for their critical systems. RAG Applications: MarkLogic provides a foundation for Retrieval-Augmented Generation. By applying MarkLogic's flexible and secure search, the application can enhance a prompt with relevant information that the user has access to. Use Cases Where MarkLogic May Not Be a Good Fit: Limited Data Volume: If your application deals with a very small amount of data, MarkLogic might be more robust than necessary. In such cases, lightweight databases like SQLite or key-value stores such as Redis may offer simpler and more cost-effective solutions. Simple Key-Value Storage: If your application primarily involves basic key-value storage with minimal querying requirements, MarkLogic might be overkill. In such cases, lightweight NoSQL databases like MongoDB may offer simpler and more cost-effective solutions. Strict Budget Constraints: While MarkLogic provides a comprehensive set of features, its enterprise-grade capabilities come at a cost. Organizations operating under strict budget constraints may find MarkLogic's licensing fees and infrastructure requirements prohibitive, especially for smaller projects with modest scalability needs. MarkLogic truly shines when applied to high-value problems. For smaller businesses or projects with a use case that we can generalize, 4V Services may be able to work with you to apply the power of MarkLogic by offering a multi-customer service. Use Cases Where MarkLogic May Be a Good Fit: Hybrid Data Environments: In environments where both structured and unstructured data coexist, MarkLogic's ability to seamlessly integrate relational and non-relational data models can offer a compelling advantage. It serves as a bridge between traditional databases and modern data lakes, providing a unified platform for diverse data types. Compliance and Regulatory Requirements: Organizations operating in regulated industries, such as healthcare and finance, often grapple with stringent compliance and security mandates. MarkLogic's granular security controls, fine-grained access permissions, and auditable data lineage features make it a viable choice for addressing regulatory requirements and ensuring data governance. Exploratory Data Analysis: For data exploration and discovery tasks where the schema is evolving or uncertain, MarkLogic's schema-agnostic approach and flexible data model can facilitate rapid prototyping and experimentation. Developers can iterate quickly without the constraints of predefined schemas, allowing for agile exploration of data-driven insights. In conclusion, the suitability of MarkLogic as a database solution depends on the specific requirements, constraints, and priorities of your project. By assessing factors such as data complexity, querying needs, budget considerations, and compliance requirements, you can determine whether MarkLogic aligns with your organization's objectives. While it may not be the optimal choice for every scenario, its unique blend of features positions it as a compelling option for enterprises grappling with the complexities of modern data management. Special Offer Are you considering MarkLogic for your next data management project? Take the next step by partnering with 4V Services for a proof-of-concept or pilot project. Companies signing on by June 30th will receive a credit toward the production implementation project that follows the PoC or pilot. Contact us today to learn more and unlock the full potential of MarkLogic for your organization's data needs. Share this post:
correct_foundationPlace_00033	FactBench	2	51	https://stackshare.io/stackups/marklogic-vs-microsoft-sql-server	en	MarkLogic vs Microsoft SQL Server	https://img.stackshare.i…5b5c035f6b86.png	https://img.stackshare.i…5b5c035f6b86.png	[]	[]	[]	[ "" ]	null	[]	null	MarkLogic - Schema-agnostic Enterprise NoSQL database technology, coupled w/ powerful search & flexible application services. Microsoft SQL Server - A relational database management system developed by Microsoft.	en		StackShare	https://stackshare.io/stackups/marklogic-vs-microsoft-sql-server
correct_foundationPlace_00033	FactBench	1	53	https://www.softwarereviews.com/categories/119/products/4467/alternatives	en	Progress MarkLogic Data Platform Alternatives and Competitors...	https://cdn1.softwarerev…e592473ae868.png	https://cdn1.softwarerev…e592473ae868.png	[ "https://cdn0.softwarereviews.com/assets/logos/info-tech/info-tech-research-group-db041b640665c6201359b0e19da6758cdeb39ea416ec6c156eecaf9bea0806bd.svg", "https://cdn1.softwarereviews.com/assets/logos/software_reviews/software_reviews_logo_centered-b3f3ddcf554938239aa2b9c4d2f16f9598dcc4ca7458ae11c4af4bd663bbbe59.svg", "https://cdn.softwarereviews.com/production/logos/offerings/4467/medium/ProgressMarkLogic_PrimaryLogo.png?1692806011", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4464/original/logo106_(2).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/6930/original/cebca6f7-13d3-4a64-be45-78a2f1504817microsoft_favicon.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4474/original/aws_(3)_(1).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4481/original/neo4j_logo_globe.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4491/original/teradata.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/7583/original/cebca6f7-13d3-4a64-be45-78a2f1504817microsoft_favicon_(1).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4464/original/logo106_(2).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/6930/original/cebca6f7-13d3-4a64-be45-78a2f1504817microsoft_favicon.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4474/original/aws_(3)_(1).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4481/original/neo4j_logo_globe.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/4491/original/teradata.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/7583/original/cebca6f7-13d3-4a64-be45-78a2f1504817microsoft_favicon_(1).png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/6605/original/SAP_fav.png", "https://softwarereviews.s3.amazonaws.com/production/favicons/offerings/6931/original/673492.png", "https://cdn.softwarereviews.com/production/logos/offerings/6930/thumb/microsoft.png?1661193514", "https://cdn.softwarereviews.com/production/logos/offerings/4464/thumb/oracle-logo.png?1617162020", "https://cdn.softwarereviews.com/production/logos/offerings/4474/thumb/1200px-Amazon_Web_Services_Logo.svg.png?1708532025", "https://cdn.softwarereviews.com/production/logos/offerings/6605/thumb/SAP_logo.png?1617153985", "https://cdn.softwarereviews.com/production/logos/offerings/4491/thumb/220px-Teradata_logo_2018.png?1617151140", "https://cdn.softwarereviews.com/production/logos/offerings/7583/thumb/microsoft.png?1692377324", "https://cdn1.softwarereviews.com/assets/content_unlock/reports@2x-6f7f0ff935458748bc2329d6299d11d06d398dfbcac8274a390d472087f580dd.png 2x", "https://cdn0.softwarereviews.com/assets/logos/info-tech/info-tech-research-group-db041b640665c6201359b0e19da6758cdeb39ea416ec6c156eecaf9bea0806bd.svg", "https://cdn1.softwarereviews.com/assets/logos/software_reviews/software_reviews_logo_centered-b3f3ddcf554938239aa2b9c4d2f16f9598dcc4ca7458ae11c4af4bd663bbbe59.svg", "https://cdn0.softwarereviews.com/assets/certifications/AICPA-SOC-certification-SM-49e82aa76f2803e873226adac80ca3d19c631e692966891132b9d71d18ae3665.png", "https://cdn1.softwarereviews.com/assets/certifications/cyber-essentials-certification-SM-642d285c0ce80cd79214beca9a32e633292909529c495435b374be828c2b1e1e.png", "https://cdn2.softwarereviews.com/assets/certifications/PwC-certification-SM-cb002966ff1cb64a04d54af78a2cca26215bdde2a8cf929d1367a031f10f3cb2.png" ]	[]	[]	[ "progress marklogic data platform", "oracle database", "microsoft sql server", "amazon aurora", "neo4j graph database", "teradata advanced sql engine", "azure cosmos db", "sap hana cloud", "ibm db2", "compare", "vs", "competitors", "altenatives", "software", "reviews", "selection" ]	null	[ "softwarereviews.com" ]	null	Top 8 Progress MarkLogic Data Platform Alternatives and Competitors • Oracle Database • Microsoft SQL Server • Amazon Aurora • Neo4j Graph Database • Teradata Advanced SQL Engine • Azure Cosmos DB • SAP HANA Cloud • IBM Db2 • Transaction Data Store	en	https://cdn1.softwarereviews.com/assets/favicon-97cfcd762bccd703ee81c00b46eaaaca98efa8857668f18e9eac7e957ea667a8.ico	SoftwareReviews	https://www.softwarereviews.com/categories/119/products/4467/alternatives	The Progress MarkLogic Data Platform is a multi-model, enterprise-grade, unified data platform for solving complex data challenges. With its flexible data model and advanced security, it integrates data from any source 10x faster in a single platform, breaking data silos while ensuring the highest levels of data protection. Coupled with powerful built-in search and semantic AI, the MarkLogic data platform delivers a 360-degree view of customers and data operations to improve the customer experience, accelerate discovery and drive smart decisions. Common Features Analytics and Reporting \| Data Virtualization \| Big Data Analytics \| Data Management \| Operations Management \| Data Protection and Security \| Data Monitoring and Administration \| Data Backup \| Data Replication \| Database Management \| Systems Performance Management \| Cloud/On-Prem/Hybrid Deployment \| Distributed Processing \| High Availability \| High Speed & Online Transaction Processing \| Hybrid Transaction and Analytical Support Apply intelligence across all your data with SQL Server 2019. Whether your data is structured or unstructured, query and analyze it using the data platform with industry-leading performance and security. Amazon Aurora is a relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Advanced SQL Engine leverages industry-leading Teradata Database which was designed with a patented massively parallel processing (MPP) architecture from ground-up. This allows complex analytics workloads to be broken down and distributed in order to perform as efficiently as possible. Advanced SQL is the foundation that provides the scalability to start small and then expand into an enterprise-wide, mission-critical analytics system. Azure Cosmos DB is a fully managed NoSQL database service for modern app development with guaranteed single-digit millisecond response times and 99.999-per cent availability, backed by SLAs, automatic and instant scalability, and open-source APIs for MongoDB and Cassandra. Harness the power of your data and accelerate trusted outcome-driven innovation by developing intelligent and live solutions for real-time decisions and actions on a single data copy. Support next-generation transactional and analytical processing with a broad set of advanced analytics – run securely across hybrid and multicloud environments. Transform and modernize your business with the leader in AI-driven data management solutions. IBM Db2® is a family of hybrid data management products offering a complete suite of AI-empowered capabilities designed to help you manage both structured and unstructured data on premises as well as in private and public cloud environments. Db2 is built on an intelligent common SQL engine designed for scalability and flexibility.
correct_foundationPlace_00033	FactBench	2	91	https://techcrunch.com/2017/05/31/ntt-data-announces-strategic-investment-in-nosql-database-provider-marklogic/	en	NTT Data announces strategic investment in NoSQL database provider MarkLogic	https://techcrunch.com/w…es-517860886.jpg	https://techcrunch.com/w…es-517860886.jpg	[ "https://techcrunch.com/wp-content/themes/tc-23/dist/svg/tc-logo.svg", "https://techcrunch.com/wp-content/uploads/2017/05/gettyimages-517860886.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/24.07.18-Kakao-founder-Brian-Kim-.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/Wiz-Founders.-Credit-Avishag-Shaar-Yashuv-e1720979215109.webp?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/monarch-Dairy-Riley.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/eric-2.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/RidhimaHeadshot.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-1662708140-e1721664527112.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/volunteer-disrupt-2024-header.png?w=1024", "https://techcrunch.com/wp-content/uploads/2022/01/GettyImages-1314979456.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2018/06/swiggy.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Fragment_TechCrunch.png?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/GettyImages-1268601565.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/pesa.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Asus-ROG-Ally-X-3.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/women-in-ai-raman.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2020/08/GettyImages-1090431902.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/President-Biden-Speaks-On-The-Economy.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/Sundar-AI-backdrop-Google-IO.png?w=947", "https://techcrunch.com/wp-content/uploads/2024/07/wazirx-app.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Arkady-Volozh-10-e1721378622488.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/fallout-4.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/crowdstrike-windows-airplane-glitch-v1.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-2161842016.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-2162660378.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Brad-Barket-Stringer.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/06/Hale_MikeMartin_79-1-e1720555795355.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/rnc-2024-report-v3.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2023/08/ev-battery-factories-2.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/faulty-crowdstrike-update.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/cisa-crowdstrike-cybersecurity-outage.jpg?w=1024" ]	[]	[]	[ "" ]	null	[ "Frederic Lardinois" ]	2017-05-31T00:00:00	NTT Data, the large Tokyo-based global IT services provider, today announced that it has made a strategic investment in database provider MarkLogic. The two companies declined to reveal the size of the investment, but Dave Ponzini, MarkLogic's EVP of Marketing and Corporate Development, tells me it was "not a huge amount but not an insignificant amount either."	en	https://techcrunch.com/w…radient.png?w=32	TechCrunch	https://techcrunch.com/2017/05/31/ntt-data-announces-strategic-investment-in-nosql-database-provider-marklogic/	NTT Data, the large Tokyo-based global IT services provider, today announced that it has made a strategic investment in database provider MarkLogic. The two companies declined to reveal the size of the investment, but Dave Ponzini, MarkLogic’s EVP of Marketing and Corporate Development, tells me it was “not a huge amount but not an insignificant amount either.” So far, MarkLogic has raised a total of more than $173 million, including a massive $102 million Series F round in 2015. MarkLogic positions itself as a database system for integrating data from various data silos, something that’s a growing problem for large enterprises as they look into how they can get the most value out of their data. Over the years (and often because of acquisitions), different groups in a company often use different database systems, and now they are looking for ways to bring all of this information together again. Typically, the way to do that is by bringing that data into a schema-less NoSQL database, which is where MarkLogic comes in. Given this focus, it’s no surprise that the company’s customer base is mostly comprised of Global 2000 companies. While MarkLogic doesn’t disclose its exact revenue numbers, Ponzini noted that annual revenue is now “north of $100 million.” NTT Data started using MarkLogic back in 2012, but mostly to build applications for its customers. The company then also started reselling the database and, according to Ponzini, this allowed MarkLogic to make inroads into the financial services market, for example. Today’s investment cements this relationship between the two companies and will allow MarkLogic to enter many of the markets in which NTT Data is very strong (like Spain), but where MarkLogic currently doesn’t have offices. There is quite a bit of overlap between the two companies’ geographical presence, though, and in those regions where both operate, NTT Data will market the database to its customers. “NTT DATA is excited to expand our strategic relationship with MarkLogic. We look forward to extending the success we have jointly experienced over the last five years in Japan to the rest of the world,” said Toshio Iwamoto, president and CEO of NTT Data, in today’s announcement. “Our ability to solve complex data integration problems by using MarkLogic’s database platform alongside intellectual capital developed by NTT DATA allows our clients to better analyze critical insights from their data in order to gain a competitive advantage in their respective marketplaces.” Only a few weeks ago, MarkLogic launched version 9 of its database. The emphasis in this release was on security, with new features like element-level permissions and redactions, for example. “We’ve always been the most secure NoSQL database,” MarkLogic EVP Joe Pasqua told me. “But the new aspect that we wanted to push was sharing with less risk.” Once you’ve brought all of your information together, the question becomes who can access it. With element-level security, enterprises can ensure that their data can be used effectively, even as some of the information remains hidden to most users.
correct_foundationPlace_00033	FactBench	1	93	https://help.marklogic.com/News/NewsItem/View/299/looking-back-on-12-years-of-constant-innovation	en	Looking Back on 12 Years of Constant Innovation	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://help.marklogic.com/__swift/themes/client/images/ml-loader.gif", "https://help.marklogic.com/Base/StaffProfile/DisplayAvatar/0/10a55b342675c1d5879436fc82aba1f7/40", "http://marklogicprod.staging.wpengine.com/wp-content/uploads/2015/03/MarkLogic-TimeLine-Rebranded.jpg", "http://marklogicprod.staging.wpengine.com/wp-content/uploads/2015/03/MarkLogic-TimeLine-Rebranded.jpg" ]	[]	[]	[ "" ]	null	[]	null				Progress.com	https://www.progress.com/resources	Enterprise-Ready in Version 1 It’s not easy to be both innovative and enterprise-ready from the start, but MarkLogic was. Initially launched as Cerisent XQE Server 1, the first version of MarkLogic carried patents for its innovative way of storing data, and also included ACID transactions, application services, and backup/restore. Very few technologies are enterprise-ready in version 1, let alone version 2, 3, or 4. Even Oracle didn’t even have many of its enterprise capabilities such as role-based security and backup recovery until version 7, more than a decade after they first started selling software. Yes, the software world moves at a faster pace right now, but that often comes with a price. In the effort to get to market faster, database companies have often decided to focus first on doing many of the easy things. Most NoSQL databases just try and ignore ACID transactions and security, even though they know those things are important. Unfortunately, it is much harder to go back and add in the enterprise features later on, and so it comes as no surprise when we see a bunch of large database companies making acquisitions in order to fill the gaps where they made sacrifices early on. Having a strong foundation is critical, and MarkLogic has had that from the start. And, in the past decade, MarkLogic has proven itself in hundreds of enterprise organizations that require an enterprise-class database. Thriving for Over a Decade How many database companies are over ten years old? The database market doesn’t have room for inferior technologies. There is a reason that the incumbent leaders – Oracle, IBM, and Microsoft – have remained dominant for so long. Most organizations don’t take their investment in a new database lightly, and MarkLogic has been able to continue to prove to be a valuable asset to organizations again and again. In fact, MarkLogic’s first customer still runs enormous clusters with hundreds of Terabytes of data on MarkLogic. And, since first launching, that organization and hundreds of others have continued to upgrade and use the innovative new features that MarkLogic comes out with in every new release. MarkLogic’s founder, Christopher Lindblad, created an innovative, patented technology over a decade ago. And now, over 12 years later, innovation is still happening every day. In our last release, we launched a feature called Bitemporal – and right now MarkLogic is the only NoSQL database to have this feature. We’re also the only NoSQL database that has an integrated triple store for storing documents, data, and triples in the same system. Take a look at how far we’ve come… The Next Twelve Years Whenever we talk about MarkLogic with people for the first time, we always end with the question, “What Will You Reimagine?” This question isn’t just for customers though. It’s also for us. The product team is already planning for the next release, MarkLogic 9, and we are again reimagining what can be done to make MarkLogic even better. And this is when we look to you, our customers, to join us and to work with us in the effort to continue our long track record of innovation in building features and solutions that matter. (And I’m actually giving you an open invitation here, we take suggestions from customers for new features very seriously, so please get in touch!) Change happens fast and we don’t expect to stop here. MarkLogic is still a young company, and we look forward to growing even stronger over the next twelve years.
correct_foundationPlace_00033	FactBench	2	29	https://www.esri.com/partners/marklogic-corporatio-a2T70000000TZOYEA4/marklogic-and-esri-i-a2d39000001mGPxAAM	en	MarkLogic and Esri Insights for ArcGIS by MarkLogic Corporation	https://webapps-cdn.esri.com/CDN/business-partners/00P3900001GM4iyEAD	https://webapps-cdn.esri.com/CDN/business-partners/00P3900001GM4iyEAD	[ "https://webapps-cdn.esri.com/CDN/business-partners/00P7000000VLE4UEAX", "https://webapps-cdn.esri.com/CDN/business-partners/00P7000000pQxUaEAK" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic is the world’s best database for integrating data from silos. Insights for ArcGIS is a web-based, data analytics platform for visualizing spatial and non-spatial data. Anyone can easily use MarkLogic’s database capabilities to scope and r	en	/content/dam/esrisites/en-us/common/favicon.ico		https://www.esri.com/partners/marklogic-corporatio-a2T70000000TZOYEA4/marklogic-and-esri-i-a2d39000001mGPxAAM	Description: MarkLogic is the world’s best database for integrating data from silos. Insights for ArcGIS is a web-based, data analytics platform for visualizing spatial and non-spatial data. Anyone can easily use MarkLogic’s database capabilities to scope and refine queries as exposed datasets for further visual analysis in ESRI Insights.
correct_foundationPlace_00033	FactBench	1	85	https://blog.knoldus.com/how-to-bring-the-data-and-document-in-marklogic/	en	How to bring the data and document in MarkLogic	https://blog.knoldus.com…ysis-concept.jpg	https://blog.knoldus.com…ysis-concept.jpg	[ "https://www.knoldus.com/wp-content/uploads/Knoldus-logo-1.png", "https://blog.knoldus.com/wp-content/uploads/2023/02/nastech-logo.svg", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/united-states.png", "https://www.knoldus.com/wp-content/uploads/2021/12/canada.png", "https://www.knoldus.com/wp-content/uploads/2021/12/singapore.png", "https://www.knoldus.com/wp-content/uploads/2021/12/netherlands.png", "https://www.knoldus.com/wp-content/uploads/2021/12/european-union.png", "https://blog.knoldus.com/wp-content/uploads/2022/07/search_icon.png", 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"https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-datastax-white.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-kmine-white.png?fit=138%2C36&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-rust-foundation-white.png?fit=138%2C43&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-scala-white-1.png?fit=107%2C46&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-snowflake-white-1.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/umbraco-1.png?fit=178%2C50&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/aws-partner-logo-1.png?fit=92%2C56&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/Microsoft-Gold-Partner_white-1.png?fit=172%2C50&ssl=1" ]	[]	[]	[ "" ]	null	[ "Pratibha Yadav" ]	2022-11-14T06:49:15+00:00	MarkLogic is a database where data and documents plays a very important role in MarkLogic to perform the queries on query console.	en	https://blog.knoldus.com…2/04/favicon.png	Knoldus Blogs	https://blog.knoldus.com/how-to-bring-the-data-and-document-in-marklogic/	Table of contents Reading Time: 4 minutes MarkLogic brings all the features you need into one unified system as it is the only Enterprise NoSQL database. MarkLogic can bring multiple heterogeneous data sources into a single platform architecture, allowing for homogenous data access. For bringing the data we need to insert the documents. On the query console, we are able to perform the query according to requirements. Bringing in the documents There are many ways to insert documents into a MarkLogic database. Available interfaces include: MarkLogic Data Hub MarkLogic Content Pump Apache Nifi REST API XQuery functions MuleSoft Data Movement SDK (Java API) Node.js API JavaScript functions Apache Kafka Content Processing Framework XCC WebDAV Explanation of available interfaces MarkLogic Data Hub: The MarkLogic Data Hub is open-source software that is used to inject data from different sources or from multiple sources. It is used to import the data as well as harmonize the data. MarkLogic Content Pump: It is a command line tool for bulk loading billions of documents into a MarkLogic database, extracting or copying the content. It helps us to make workflow integration very easy. Apache Nifi: It is useful when someone needs to ingest data from a relational database into a MarkLogic Database. REST API: It provides a programming language agnostic way to write a document in MarkLogic. XQuery functions: When we want to write the document to a MarkLogic database then this function is used. Able to write the records from the query console or from the XQuery application. MuleSoft: The Marklogic connector for MuleSoft is Used to bring data from various other systems into the MarkLogic database. Available Interfaces Data Movement SDK (Java API): Included in the java API, the data movement SDK provides the classes for java developers to use to import and transform documents. Node.js API: It provides Node.js classes for the developers to use to write the document to a MarkLogic database from their Node.js code. JavaScript functions: Able to write the document through the query console or by using the javascript application. Apache Kafka: When we need to stream the data into the database, we can do it by using the Kafka MarkLogic connector. Content Processing Framework: A Pipeline framework for making changes to documents as they are being loaded into the database, such as enriching the data or transforming the PDF or MS office document in XML. XML Contentbase Connector (XCC): If you need to create a multi-tier application that communicates with the MarkLogic then it is useful. WebDAV: Web Distributed Authoring and Versioning used to drag and drop the documents in the Marklogic Database. Inserting the document using the Query Console To insert the document using the query console javaScript or XQuery used. The xdmp.documentLoad() function. Used to load the document from the file system into a database. declareupdate(); xdmp.documentLoad("path of the source file") Running a JavaScript expression that makes changes to a database. Need to use the declareUpdate function. The xdmp.documentinsert() function is used to write a document into a database. declareUpdate(); xdmp.documentInsert('/employee1.json', { 'title : 'Knoldus' , 'description': 'Amazing place to work' }); Uniform Resource Identifier (URI) To address any document in a MarkLogic database, it is necessary that each document has a unique URI. /products/1.json The URI does not refer to the physical location of a document in a database. Provides a unique name for referencing the document. Deleting the documents The clear button in the admin interface can be used to delete all the documents in a database. To delete an individual document, the xdmp.documentDelete() function can be used. declareUpdate(); xdmp.documentDelete('/employee1.json'); Accessing a Document To read a document in a database, use the cts.doc(). cts.doc('/employee1.json); Modifying Documents Documents can be modified via various APIS and tools, including data hub, JavaScript, XQuery, etc. JavaScript functions for updating documents include: xdmp.nodeReplace() xdmp.nodeInsert() xdmp.nodeInsertBefore() xdmp.nodeInsertAfter() xdmp.nodeDelete() Conclusion MarkLogic is a NoSql database with many facilities and if someone wants to insert the data then this blog is helpful. After insertion needs to access and modify the document by using some predefined functions. Reference: https://docs.marklogic.com/guide/ingestion/intro https://docs.marklogic.com/guide/concepts/data-management https://www.udemy.com/course/marklogic-fundamentals/learn/lecture/4793940#overview Related Written by Pratibha Yadav Pratibha Yadav is a Software consultant at Knoldus and started her beautiful journey of a career in an environment where she able to sharp - up her skills day by day by learning and earning new opportunities. She completed her Post-graduation from Sharda University, Greater Noida. She is passionate about her work and has knowledge of various programming languages. She is recognized as a quick learner, problem solver, public speaker, dedicated and responsible professional employee. Her hobbies are Writing, Reading, and spending some time with nature.
correct_foundationPlace_00033	FactBench	2	87	https://www.applytosupply.digitalmarketplace.service.gov.uk/g-cloud/services/282303637701217	en	MarkLogic Data Hub Service (DHS)	https://www.applytosupply.digitalmarketplace.service.gov.uk/static/images/favicon.ico	https://www.applytosupply.digitalmarketplace.service.gov.uk/static/images/favicon.ico	[]	[]	[]	[ "" ]	null	[]	null		en	/static/images/favicon.ico		null	We use some essential cookies to make this service work. We’d also like to use analytics cookies so we can understand how you use the service and make improvements.
correct_foundationPlace_00033	FactBench	2	68	https://www.telerik.com/	en	.NET Components Suites & JavaScript UI Libraries	https://www.telerik.com/…erik1200x630.png	https://www.telerik.com/…erik1200x630.png	[ "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/hero-02.jpg?sfvrsn=116f9eef_9", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/home-mobile-hero.svg?sfvrsn=6e5f3d51_5", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/ninja-and-kendoka.svg?sfvrsn=1e0b0ec1_13", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/mobile-ninja_kendoka.svg?sfvrsn=e19887eb_3", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/devcraft.svg?sfvrsn=e7e590c9_7", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/ninja.svg?sfvrsn=699402b1_13", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/homepage/mobile-ninja.svg?sfvrsn=e0a2ea74_3", 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"https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u23.png?sfvrsn=8e2aec56_1", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u26.png?sfvrsn=e293d4dc_1", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u20.png?sfvrsn=9d939e2e_1", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/c2-leader-fall-2022.png?sfvrsn=519d7874_6", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u8.png?sfvrsn=d55b0762_1", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u11-1.png?sfvrsn=c6843b79_1", "https://d585tldpucybw.cloudfront.net/sfimages/default-source/awards/u12-2.png?sfvrsn=78625c69_1", "https://www.telerik.com/sfimages/default-source/homepage/webinar_image.png?Status=Temp&sfvrsn=7a7c6606_2" ]	[]	[]	[ "" ]	null	[]	null	Save time building sleek web, mobile and desktop apps with professional .NET UI Components, JavaScript UI Libraries, Reporting and Automated Testing solutions.	en	/favicon.ico?v=rebv1	Telerik.com	https://www.telerik.com/	The demand for better user experiences continues to grow, while the time you have to develop a high quality, modern and engaging application continues to shrink. Stop sweating over UI and focus on the parts of the application where you can truly make a difference. Like you, we are developers. Our purpose in life is to make developers superheroes by enabling you to deliver more than expected, faster than expected. For nearly two decades, we have been partnering with our community of over three million developers to help cut down on development time, increase productivity, and make it easy to embrace the latest technologies and user experience trends. By using the modern, feature-rich and professionally designed UI components from Telerik and Kendo UI, you will be armed with everything you need to deliver outstanding web, mobile and desktop experiences in less time. With the backing of our legendary technical support, provided directly by the developers who build the products, you can be confident that you have the best partner to rely on in your journey. Using Telerik UI, we were able to boost our speed to production by over 50%. The ability to create rich, interactive UI's without the hassle of rolling our own controls has been incredibly valuable. It should also be noted that Telerik's online documentation is rich with examples, tutorials, and real-working demos. When using competing products, I found their example's to be demo-ware, and not as easily converted to actual production-ready solutions. Telerik has rich collection of components that enables developers to build fully functional and great looking web applications in a matter of days, which used to be weeks and months without Telerik. On top of that, an aggressive release cycle and very responsive support makes it one of the best investments we've made. With new controls being released every quarter, the value we get from our DevCraft Complete subscription is great. Telerik support is unsurpassed, with support forums for instant answers and an excellent ticketing system for the odd occasion when we need a little more hand-holding.
correct_foundationPlace_00033	FactBench	1	46	https://www.starfishetl.com/connect/MarkLogic/Microsoft%2520Access	en	Connect MarkLogic and Microsoft Access	https://www.starfishetl.…avicon-32x32.png	https://www.starfishetl.…avicon-32x32.png	[ "https://www.starfishetl.com/sites/default/files/logo_website.png", "https://www.starfishetl.com/sites/default/files/2022-06/secure_connections.png", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-04/200_migration-step-1.png?itok=EsnyMJAM", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-06/2_define.png?itok=sx98RdOu", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-06/3_modify.png?itok=V06OJGDY", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-04/200_migration-step-3.png?itok=RcTbieOy", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-06/5_run.png?itok=ptT__RKp", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-06/6_keys.png?itok=aU-riDDD", "https://www.starfishetl.com/sites/default/files/styles/thumbnail_75_x_75_/public/2022-06/7_run_integration.png?itok=NpjHsbrq", "https://www.starfishetl.com/sites/default/files/styles/product_logo/public/2022-07/marklogic-logo.png?itok=v7FIQPvA", "https://www.starfishetl.com/sites/default/files/styles/product_logo/public/2022-01/Microsoft_Office_Access.png?itok=lRTg5Avs", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20americas%20enterprise%20fall%202023.png?itok=B1dVtHM7", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20mid%20market%20americas%20fall%202023.png?itok=2V3h_BEd", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20americas%20fall%202023.png?itok=jWRY8Q8q", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20americas%20enterprise%20fall%202023.png?itok=B1dVtHM7", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20mid%20market%20americas%20fall%202023.png?itok=2V3h_BEd", "https://www.starfishetl.com/sites/default/files/styles/awards/public/2023-09/high%20performer%20americas%20fall%202023.png?itok=jWRY8Q8q", "https://www.starfishetl.com/sites/default/files/styles/blog_small/public/2024-07/AdobeStock_736958497-min.jpeg?h=af08213a&itok=nofOSouE" ]	[]	[]	[ "" ]	null	[]	2024-07-10T13:57:52-05:00	Seamlessly connect your MarkLogic and Microsoft Access data today. Translate insights from these apps with speed and security to grow your business.	en	/sites/default/files/favicons/apple-touch-icon.png		https://www.starfishetl.com/connect/MarkLogic/Microsoft%20Access	MarkLogic and Microsoft Access Integration Integrate MarkLogic and Microsoft Access to boost your analytical power, align your teams, and create more omnichannel experiences across your business. StarfishETL makes the process seamless with a library of pre-configured maps at your fingertips and easy ways to customize your project. Check out the visual below to learn how a typical integration flows. Then, contact our team to request a quote on your MarkLogic and Microsoft Access project. Set up access to each system Define processes & stages Modify integration & add custom fields Test integration Run initial data migration load Ensure keys are matching between systems Start integration Blog 4 Ways to Lower the Cost of Your Infor Syteline and Non-Infor CRM Integration Infor Syteline and Infor CRM integration is free in the cloud, but it is an extra charge for on-premises customers. If you aren’t using Infor CRM and want to integrate, you must use another tool. This blog explores the integration of Infor Syteline with a non-Infor CRM and offers our top five… Register Online
correct_foundationPlace_00033	FactBench	2	44	https://github.com/marklogic/java-client-api	en	api: Java client for the MarkLogic enterprise NoSQL database	https://opengraph.githubassets.com/282583450a757b233244b78ee4a7126c539648495839ccbe69942965a4b17572/marklogic/java-client-api	https://opengraph.githubassets.com/282583450a757b233244b78ee4a7126c539648495839ccbe69942965a4b17572/marklogic/java-client-api	[ "https://camo.githubusercontent.com/c22b66b757fd8aa4226bb6fa07c905f8edd90b0229de247bbab6220165ab0e24/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6d61726b6c6f6769632f6a6176612d636c69656e742d6170692e737667", "https://camo.githubusercontent.com/22c1c449922d0dfa3f0f7a6591848fda26e26df1251f83b0dea0da82865c9a2b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f6d61726b6c6f6769632f6a6176612d636c69656e742d6170692e737667", "https://camo.githubusercontent.com/db9dfde8049c5d66ba62fde707d2cfb30e26f9f26ff274c3442c0aec1ec410a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667", "https://camo.githubusercontent.com/37e134ba952e39219261babced856695dd98231c4948a32f1cf84494b561e10f/68747470733a2f2f736e796b2e696f2f746573742f6769746875622f6d61726b6c6f6769632f6a6176612d636c69656e742d6170692f62616467652e737667", "https://avatars.githubusercontent.com/u/298950?s=64&v=4", "https://avatars.githubusercontent.com/u/5026193?s=64&v=4", "https://avatars.githubusercontent.com/u/8634482?s=64&v=4", "https://avatars.githubusercontent.com/u/7087293?s=64&v=4", "https://avatars.githubusercontent.com/u/19510878?s=64&v=4", "https://avatars.githubusercontent.com/u/14857031?s=64&v=4", "https://avatars.githubusercontent.com/u/1747973?s=64&v=4", "https://avatars.githubusercontent.com/u/5034960?s=64&v=4", "https://avatars.githubusercontent.com/u/17153443?s=64&v=4", "https://avatars.githubusercontent.com/u/12160092?s=64&v=4", "https://avatars.githubusercontent.com/u/382672?s=64&v=4", "https://avatars.githubusercontent.com/u/2940775?s=64&v=4", "https://avatars.githubusercontent.com/u/8721137?s=64&v=4", "https://avatars.githubusercontent.com/u/23352472?s=64&v=4" ]	[]	[]	[ "" ]	null	[]	null	Java client for the MarkLogic enterprise NoSQL database - marklogic/java-client-api	en	https://github.com/fluidicon.png	GitHub	https://github.com/marklogic/java-client-api	The MarkLogic Java Client makes it easy to write, read, delete, and find documents in a MarkLogic database. The client requires connecting to a MarkLogic REST API app server and is ideal for applications wishing to build upon the MarkLogic REST API. The client supports the following core features of the MarkLogic database: Write and read binary, JSON, text, and XML documents. Query data structure trees, marked-up text, and all the hybrids in between those extremes. Project values, tuples, and triples from hierarchical documents and aggregate over them. Patch documents with partial updates. Use Optimistic Locking to detect contention without creating locks on the server. Execute ACID modifications so the change either succeeds or throws an exception. Execute multi-statement transactions so changes to multiple documents succeed or fail together. Call Data Services by means of a Java interface on the client for data functionality implemented by an endpoint on the server. The client can be used in applications running on Java 8, 11, and 17. If you are using Java 11 or higher and intend to use JAXB, please see the section below for ensuring that the necessary dependencies are available in your application's classpath. To use the client in your Maven project, include the following in your pom.xml file: To use the client in your Gradle project, include the following in your build.gradle file: Next, read The Java API in Five Minutes to get started. Full documentation is available at: Java Application Developer's Guide JavaDoc If you are using Java 11 or higher (including Java 17) and you wish to use JAXB with the client, you'll need to include JAXB API and implementation dependencies as those are no longer included in Java 11 and higher. For Maven, include the following in your pom.xml file: For Gradle, include the following in your build.gradle file (this can be included in the same dependencies block as the one that includes the marklogic-client-api dependency): You are free to use any implementation of JAXB that you wish, but you need to ensure that you're using a JAXB implementation that corresponds to the javax.xml.bind interfaces. JAXB 3.0 and 4.0 interfaces are packaged under jakarta.xml.bind, and the Java API does not yet depend on those interfaces. Thus, you are free to include an implementation of JAXB 3.0 or 4.0 in your project for your own use; it will not affect the Java API. A caveat though is if you are trying to use different major versions of the same JAXB implementation library - such as org.glassfish.jaxb:jaxb-runtime - then you will run into an expected dependency conflict between the two versions of the library. This can be worked around by using a different implementation of JAXB 3.0 or JAXB 4.0 - for example: The client will soon be updated to use the newer jakarta.xml.bind interfaces. Until then, the above approach or one similar to it will allow for both the old and new JAXB interfaces and implementations to exist together in the same classpath.
correct_foundationPlace_00033	FactBench	2	52	https://www.thestack.technology/with-a-355m-buyout-of-marklogic-2023s-first-big-software-deal-is-in-and-it-may-be-a-harbinger/	en	Progress's $355m move for MarkLogic sets the tone for 2023	https://www.thestack.tec…9c0-unsplash.jpg	https://www.thestack.tec…9c0-unsplash.jpg	[ "https://www.thestack.technology/content/images/2024/05/the-stack-logo-black-green-stroke-rgb-900px-w-72ppi.png", "https://www.thestack.technology/content/images/2024/05/the-stack-logo-black-green-stroke-rgb-900px-w-72ppi.png", "https://www.thestack.technology/content/images/size/w1304/wp-content/uploads/2023/01/taras-hrytsak-viefakw-9c0-unsplash.jpg", "https://www.thestack.technology/content/images/wp-content/uploads/2023/01/marklogic.png", "https://www.thestack.technology/content/images/wp-content/uploads/2023/01/olap-vs-oltp-database-comparison.png", "https://www.thestack.technology/content/images/size/w1304/2024/06/kevin-ku-w7ZyuGYNpRQ-unsplash.jpg", "https://www.thestack.technology/content/images/size/w1304/wp-content/uploads/2023/05/img_1312-scaled.jpg", "https://www.thestack.technology/content/images/size/w1304/wp-content/uploads/2023/05/ebay_campus.jpg", "https://www.thestack.technology/content/images/size/w1304/wp-content/uploads/2023/05/elton-john-x-vodafone-ar-performance_5_med-res.jpg", "https://www.thestack.technology/content/images/size/w1304/2024/07/22884a44-27f2-4315-bdbd-6528915235cd.webp", "https://www.thestack.technology/content/images/size/w1304/2024/07/greg-bulla-lKjX3S4pdog-unsplash.jpg", "https://www.thestack.technology/content/images/size/w1304/2024/07/patrick-pahlke-chymBT2shME-unsplash.jpg", "https://www.thestack.technology/content/images/size/w1304/2024/07/rich-dahlgren--frJ4gQ2bTw-unsplash.jpg", "https://www.thestack.technology/content/images/2024/05/the-stack-logo-black-green-stroke-rgb-900px-w-72ppi.png", "https://www.thestack.technology/content/images/2024/05/the-stack-logo-black-green-stroke-rgb-900px-w-72ppi.png" ]	[]	[]	[ "" ]	null	[ "Edward Targett" ]	2023-01-04T12:33:11+00:00	MarkLogic has a marquee customer base of blue chips and is a Magic Quadrant contendor. But it's little known and now agreed an acquisition.	en	https://www.thestack.tec…----BLACK-v4.png	The Stack	https://www.thestack.technology/with-a-355m-buyout-of-marklogic-2023s-first-big-software-deal-is-in-and-it-may-be-a-harbinger/	The first big software deal of 2023 is in, with Progress agreeing to buy NoSQL database and metadata management company MarkLogic for $355 million. Despite sitting in the Gartner Magic Quadrant for Cloud Database Management Systems with the hyperscalers, MarkLogic has low visibility, but a reputation for unique “data agility” capabilities, a sticky blue chip customer base (Airbus, J.P. Morgan, Nike, Merck) and a strong reputation for customer service: Customers will be hoping that the latter continues post-acquisition. MarkLogic describes itself as a “multi-model database” that combines document, semantic graph, geospatial, and relational models (native storage for JSON, XML, text, RDF triples, geospatial, and binaries (e.g., PDFs, images, videos) into a single, scalable, high-performance and ACID-capable operational database. The company cites “a major investment bank” as a key customer (we’d speculate wildly that this is J.P. Morgan) which uses it to underpin its derivatives trade store and swapped out a total of 20 Oracle and Sybase databases for MarkLogic. Progress agrees to buy MarkLogic: A little about both Progress, listed on the Nasdaq, is a large provider of application development and infrastructure software, with over 100,000 enterprise customers and more than 2,000 employees in 20+ countries. It expects the deal – which executives said would give it a “best-in-class, proprietary, multi-model NoSQL database, along with robust semantic metadata management and AI capabilities” -- to add $100 million in ARR to its bottom line. MarkLogic positions itself as a NoSQL database system for integrating data from various data silos and improving “data agility”. It was last year named a visionary in its Magic Quadrant and made a significant acquisition itself in November 2021 with the buyout of metadata management firm Smartlogic. MarkLogic’s revenues appear to have stagnated at around $100 million for some years, but it has been innovating hard regardless, recently for example adding tools that support the indexing and querying of geospatial data, scalable export of large geospatial result sets, and interoperability with GIS tools. CEO Jeff Casale told staff: “Progress understands the value MarkLogic brings to the database and semantic metadata management customers. It will bring significant cross-functional corporate resources to bear in securing the long-term success of our customers and our people.” They would be forgiven for feeling just a little twitchy at Progress’s comments to investors that the deal is “expected to provide an opportunity for Progress to leverage its highly disciplined operating model and infrastructure to maximize efficiency”. Watch this space to see how that pans out. Progress MarkLogic deal: A sign of deals to come in 2023? The acquisition as 2023 barely gets warmed up may be a sign of things to come. As EY suggested in late 2022 “faced with high inflation, an energy crisis and falling consumer confidence, the biggest opportunity for tech companies in 2023 is to adopt an active M&A strategy.” As EY's Olivier Wolf, a Global TMT Strategy and Transactions Leader, put it on December 7: "The deal market has slowed due to macro headwinds and financial volatility, but this has improved opportunities for corporate buyers with strong balance sheets. In turn, competition for targets should heat up again next year, as hundreds of billions of private equity dollars come to the market. Transformative acquisitions could launch tech companies into new markets or adjacent verticals like HealthTech, and accretive acquisitions have the potential to strengthen portfolios with leading-edge technologies like artificial intelligence.” Don't miss out on our events and news: Follow The Stack on LinkedIn One observer of the database market suggests bumpy times ahead after rampant raising in 2021. Dr Andy Pavlo, Associate Professor of Databaseology (really) in the Computer Science Department at Carnegie Mellon University and co-founder of OtterTune, a database tuning company, noted in a thought-provoking blog at the close of 2022: "The bad news is that these companies [DBMS startups] are in trouble unless the tech sector improves and big institutional investors start turning their money out on the street again. "The market cannot sustain so many independent software vendors (ISVs) for databases... They are too expensive for acquisition (unless the VCs are willing to take massive cuts) for most companies. Furthermore, the major tech companies (e.g., Amazon, Google, Microsoft) that do large M&A’s already have their own cloud database offerings. Hence, it is not clear who will acquire these database start-ups. It does not make sense for Amazon to buy Clickhouse at their 2021 $2b valuation when they are already making billions per year from Redshift. This problem is not exclusive to OLAP [online analytical processing] database companies; OLTP [online transaction processing] database companies will face the same issue soon.I am not the only one making such dire predictions about the fate of database start-ups." "Gartner analysts predict that 50% of independent DBMS vendors will go out of business by 2025. I am obviously biased, but I think the companies that will survive will be the ones that work in front of DBMSs to improve/enhance them rather than replace them (e.g., dbt, ReadySet, Keebo, and OtterTune)," he added. Your views? Get in touch and let us know!
correct_foundationPlace_00033	FactBench	1	4	https://aws.amazon.com/marketplace/pp/prodview-nzuovlj2xtbua	en	Model Database: Developer Edition v. 9	https://d32gc0xr2ho6pa.c…ocialPreview.png	https://d32gc0xr2ho6pa.c…ocialPreview.png	[ "https://d7umqicpi7263.cloudfront.net/img/product/05024da6-de2b-415c-96ba-21509abaff4f.com/d2591dce87f0b868f03e5ee96382dfe0", "https://d7umqicpi7263.cloudfront.net/img/product/05024da6-de2b-415c-96ba-21509abaff4f.com/d2591dce87f0b868f03e5ee96382dfe0" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic Server is the agile, scalable, and secure foundation of the MarkLogic Data Platform. A multi-model database with a wide array of enterprise-...	en	https://d32gc0xr2ho6pa.cloudfront.net/img/general/favicon.ico		https://aws.amazon.com/marketplace/pp/prodview-nzuovlj2xtbua	MarkLogic Server is the agile, scalable, and secure foundation of the MarkLogic Data Platform. A multi-model database with a wide array of enterprise-level data integration and management features, MarkLogic helps you create value from complex data - faster. MarkLogic Server natively stores JSON, XML, text, geospatial, and semantic data in a single, unified data platform. This ability to store and query a variety of data models provides unprecedented flexibility and agility when integrating data from silos. MarkLogic is the best, most comprehensive database to power an enterprise data platform. MarkLogic Server is built to securely integrate data, track it through the integration process, and safely share in it in its curated form. Meet business-critical goals and accelerate innovation with MarkLogic. MarkLogic Server natively stores JSON, XML, text, geospatial, and semantic triples in one unified platform. This ability to store and query variety of data models result in unprecedented flexibility and agility when integrating data from silos. It is the best database to power an enterprise data hub. The Developer Edition includes all features but is limited to pre-production applications and 1TB in data size. This AMI can also be used with existing licenses (BYOL.) MarkLogic Server is built to securely integrate data, track it through integration process, and safely share its curated form. MarkLogic enables all data professionals to meet their mission-critical goals. Learn more at https://www.marklogic.com/product/data-hub-service/.
correct_foundationPlace_00033	FactBench	1	50	https://www.prnewswire.com/news-releases/expert-system-and-marklogic-corporation-join-forces-for-cognitive-information-management-applications-300265781.html	en	Expert System and MarkLogic Corporation Join Forces for Cognitive Information Management Applications	https://photos.prnewswire.com/prnthumb/20151118/288932LOGO	https://photos.prnewswire.com/prnthumb/20151118/288932LOGO	[ "https://www.prnewswire.com/content/dam/prnewswire/homepage/prn_cision_logo_desktop.png", "https://www.prnewswire.com/content/dam/prnewswire/homepage/prn_cision_logo_mobile.png", "https://mma.prnewswire.com/media/1283900/Expert_ai_Logo.jpg?w=300", "https://www.prnewswire.com/content/dam/prnewswire/subject-and-industry-code-images/CPR.jpg", "https://www.prnewswire.com/content/dam/prnewswire/subject-and-industry-code-images/STW.jpg", "https://www.prnewswire.com/content/dam/prnewswire/subject-and-industry-code-images/STW.jpg", "https://www.prnewswire.com/content/dam/prnewswire/subject-and-industry-code-images/JVN.jpg" ]	[]	[]	[ "Expert System" ]	null	[ "Expert System" ]	2016-05-10T06:00:00-04:00	/PRNewswire/ -- Expert System (EXSY.MI), the leader in multilingual cognitive computing technology for the effective management of unstructured information,...	en	/content/dam/prnewswire/icons/2019-Q4-PRN-Icon-32-32.png		https://www.prnewswire.com/news-releases/expert-system-and-marklogic-corporation-join-forces-for-cognitive-information-management-applications-300265781.html	ROCKVILLE, Md., May 10, 2016 /PRNewswire/ -- Expert System (EXSY.MI), the leader in multilingual cognitive computing technology for the effective management of unstructured information, today announced a partnership with MarkLogic Corporation, a leading operational and transactional Enterprise NoSQL database provider. The integration of Expert System's Cogito multilingual cognitive software will enable MarkLogic® database users to add cognitive capabilities to their information applications. Cogito's powerful patented semantic technology reads and understands text the way humans do. It unambiguously recognizes relevant information and enriches content with domain-specific data such as entities, relationships, topics and categories. Cogito brings organizations the benefits of more effective search, linking, analytics and visualizations, and enables them to discover actionable insights to optimize their business and improve their decision making. "We're excited to be partnering with MarkLogic, one of the world's leading database providers," said Daniel Mayer, CEO of Expert System Enterprise. "Our technologies are a natural fit, delivering smarter information management workflows and creating a foundation for competitive advantage for any organization." Expert System is also a sponsoring company at the upcoming MarkLogic World 2016 in San Francisco, May 9 – 12. At MarkLogic World 2016, participants can learn how Expert System, in conjunction with the MarkLogic database, can help customers integrate, discover and leverage their data in support of their industry leadership. "Our customers are leapfrogging competitors, as they understand that innovation no longer needs to be stymied by data silos. They are out-innovating their peers by using the MarkLogic® database," said David Ponzini, Senior Vice President, Marketing & Corporate Development, MarkLogic. "We're excited to partner with Expert System to integrate and make sense of data." About MarkLogic World MarkLogic World is a series of conferences around the globe designed to connect like-minded innovators who understand the importance of establishing a platform that can provide comprehensive, up-to-date information anytime, anywhere. Through hands-on workshops, technical breakouts, in-person training courses, and peer-to-peer sessions and networking, MarkLogic World attendees will learn how MarkLogic's operational and transactional Enterprise NoSQL database platform empowers enterprises and organizations in financial services, healthcare, media and entertainment, government, energy, manufacturing and many more build next generation applications on a unified, 360-degree view of their data. About Expert System Expert System is a leading provider of cognitive computing and text analytics software based on the proprietary, patented, multilingual semantic technology of Cogito. Using Expert System's products, enterprise companies and government agencies can go beyond traditional keyword approaches for the rapid sense making of their structured and unstructured data. Expert System technology has been deployed to deliver solutions for a vast range of business requirements such as semantic search, open source intelligence, multilingual text analytics, natural language processing and the development and management of taxonomies and ontologies. Expert System serves some of the world's largest industries including Banking and Insurance, Life Sciences and Pharmaceuticals, Oil and Gas, Media and Publishing, and Government including companies such as Shell, Chevron, Eli Lilly, Networked Insights, Nalco Champion, US Department of Justice, DTRA, BAnQ, Biogen, Bloomberg BNA, Elsevier, Gannett, IMF, RSNA, P\S\L, Sanofi, SOQUIJ, The McGraw-Hill Companies, Thomson Reuters, U.S. Department of Agriculture, Wiley and Wolters Kluwer. For more information visit www.expertsystem.com or follow us on Twitter at @Expert_System MarkLogic is a registered trademark of MarkLogic Corporation in the United States and/or other countries. *Other names and brands may be claimed as the property of others. Logo - http://photos.prnewswire.com/prnh/20151118/288932LOGO SOURCE Expert System Related Links
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the realm of Databases, offering a unique approach to managing and leveraging data. Known for its versatility and scalability, MarkLogic is a leading choice for organizations seeking powerful data management solutions. With its ability to handle structured and unstructured data alike, MarkLogic empowers users to unleash the full potential of their information assets. MarkLogic falls into the category of Databases, specializing in providing a robust framework for storing, retrieving, and managing data efficiently. Unlike traditional relational databases, MarkLogic boasts the capability to handle diverse data types, making it ideal for organizations dealing with complex and varied datasets. Its advanced features, such as ACID compliance and full-text search capabilities, set it apart as a versatile and reliable database solution. Founded in 2001 by Christopher Lindblad, Mary Wiecki, and Paul Pedersen, MarkLogic emerged with the vision of revolutionizing the way organizations interact with their data. The founders aimed to address the challenges posed by the exponential growth of unstructured data, envisioning a database system that could bridge the gap between structured and unstructured information seamlessly. This led to the inception of MarkLogic, which has since become a cornerstone in the data management landscape. In terms of current market share, MarkLogic has carved out a significant presence within the database technology sector. With a loyal customer base spanning various industries, MarkLogic continues to showcase steady growth and adoption. As organizations increasingly recognize the importance of leveraging unstructured data for insights and decision-making, the demand for technologies like MarkLogic is poised to rise. Market forecasts suggest a positive trajectory for MarkLogic, anticipating continued growth and relevance in the evolving data management landscape.
correct_foundationPlace_00033	FactBench	1	27	https://www.progress.com/resources/videos/abn-amro-building-an-agile-data-foundation	en	ABN AMRO: Building an Agile Data Foundation	https://www.progress.com…social-image.png	https://www.progress.com…social-image.png	[ "https://www.progress.com/images/default-source/resource-center/on-demand-webinars-video-hero-background.png?sfvrsn=e4b7bc0c_11" ]	[ "https://youtu.be/dsiiw-2E3EI" ]	[]	[ "" ]	null	[]	null	Check out ABN AMRO: Building an Agile Data Foundation video and learn more about Progress products.	en	/favicon.ico?v=2	Progress.com	https://www.progress.com/resources/videos/abn-amro-building-an-agile-data-foundation	Data Platform Accelerate data, AI and analytics projects, manage costs and deliver enterprise growth with the Progress Data Platform. Digital Experience Real solutions for your organization and end users built with best of breed offerings, configured to be flexible and scalable with you. Infrastructure Management Progress infrastructure management products speed the time and reduce the effort required to manage your network, applications and underlying infrastructure. Federal Solutions Software products and services for federal government, defense and public sector.
correct_foundationPlace_00033	FactBench	2	25	https://www.sitefusion.de/en/up-tp-date-with-news-from-sitefusion/sitefusion-and-marklogic-enter-a-oem-partnership/	en	SiteFusion and MarkLogic enter a OEM partnership	https://www.sitefusion.d…nda-postsize.jpg	https://www.sitefusion.d…nda-postsize.jpg	[ "https://www.sitefusion.de/wp-content/themes/child-theme/img/logo-sitefusion.png", "https://www.sitefusion.de/wp-content/plugins/sitepress-multilingual-cms/res/flags/de.png", "https://www.sitefusion.de/wp-content/uploads/2019/08/keyvisual-ohne-camunda-headersize-1920x535.jpg" ]	[]	[]	[ "" ]	null	[ "dietl" ]	2020-06-19T10:26:17+00:00	SiteFusion and US enterprise XML-/NoSQL database developer MarkLogic entered a strategic alliance.	en	https://www.sitefusion.d…e-icon-57x57.png	SiteFusion - made for publishers: Best-of-Breed Content Management	https://www.sitefusion.de/en/up-tp-date-with-news-from-sitefusion/sitefusion-and-marklogic-enter-a-oem-partnership/	SiteFusion and US enterprise XML-/NoSQL database developer MarkLogic entered a strategic alliance, in order to offer European publishers a fully integrated solution for their processes on the basis of the state-of-art technology. Right on time for the Frankfurter Buchmesse 2018 SiteFusion and MarkLogic announce their OEM partnership. SiteFusion Release 6 includes, instead of the previous regional data base, the XML-/ NoSQL database MarkLogic out-of-the-box. To master the future challenges in the area of XML processing and to ensure new possibilities for data management, key wording and the assembling, SiteFusion has decided to take this important step. Especially in the field of very large amount of data, data sourced out of several silos as well as in the area of search and semantic enrichment of documents, MarkLogic offeres possibilities, which are not available on the basis of a regional database in this form. The storage options of the data are sophisticated: XML and JSON documents, RDF Triples, geo, binary as well as metadata can be stored and processed in a structured or unstructured way. MarkLogic’s state-of-the-art database is especially designed for fast changes of data assets, allowing data to be integrated up to four times faster than a relational database. “The investment into our system, which we are doing with the transition to a whole new way of data storage, is certainly large – initial implementation already showed that this investment would be worthwhile. The boost of performance we get from integrating data into our CMS through MarkLogic is enormous. Within seconds, XML documents with tens of thousands of pages can be imported, processed and exported again. This benefits our customers as well as integration partners directly”, says Mario Kandler, CEO of SiteFusion. “We are pleased to have SiteFusion as a new OEM partner. By migrating a relational database to our operational and transactional NoSQL database technology, the company is relies on a future-proof technology while simultaneously laying out the central groundwork for greater agility and flexibility. More than anything, SiteFusion users benefit immediately from the speed, which can be accomplished now in data integration and data management.”, said Andreas Rottenaicher, Director Alliances DACH, MarkLogic.
correct_foundationPlace_00033	FactBench	1	70	https://db-engines.com/en/system/Apache%2BDruid%253BMarkLogic%253BSphinx	en	Apache Druid vs. MarkLogic vs. Sphinx Comparison			[ "https://db-engines.com/matomo/matomo.php?idsite=2&rec=1", "https://db-engines.com/db-engines.png", "https://db-engines.com/pictures/extremedb/extremedb-hard-realtime.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/ranking_trend_s.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/pictures/Email.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/Facebook.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/Mastodon.svg", "https://db-engines.com/pictures/Bluesky.png", "https://db-engines.com/pictures/Neo4j-logo_color_sm.png", "https://db-engines.com/pictures/datastax-fp.png", "https://db-engines.com/pictures/milvus.svg", "https://db-engines.com/pictures/singlestore_250x80.png", "https://db-engines.com/pictures/raimadb.png" ]	[]	[]	[ "" ]	null	[]	null	Detailed side-by-side view of Apache Druid and MarkLogic and Sphinx	en			null	Intelligence for multi-domain warfighters can now be sourced from logistics operations 13 May 2024, Breaking Defense Seven Quick Steps to Setting Up MarkLogic Server in Kubernetes 1 February 2024, release.nl Progress's $355m move for MarkLogic sets the tone for 2023 4 January 2023, The Stack Progress to acquire PE-backed data platform MarkLogic for $355m 4 January 2023, PE Hub Progress Completes Acquisition of MarkLogic 7 February 2023, GlobeNewswire
correct_foundationPlace_00033	FactBench	2	33	https://www.linkedin.com/pulse/turning-marklogic-next-oracle-kurt-cagle	en	Turning MarkLogic Into the Next Oracle	https://media.licdn.com/dms/image/C4E12AQEEsPlaj0VBGw/article-cover_image-shrink_600_2000/0/1520150092199?e=2147483647&v=beta&t=Ofbg0ExWga1ufcI9QpFE8ShMQjSMepFH13Udlpx6CKY	https://media.licdn.com/dms/image/C4E12AQEEsPlaj0VBGw/article-cover_image-shrink_600_2000/0/1520150092199?e=2147483647&v=beta&t=Ofbg0ExWga1ufcI9QpFE8ShMQjSMepFH13Udlpx6CKY	[ "https://media.licdn.com/dms/image/C4E12AQEEsPlaj0VBGw/article-cover_image-shrink_600_2000/0/1520150092199?e=2147483647&v=beta&t=Ofbg0ExWga1ufcI9QpFE8ShMQjSMepFH13Udlpx6CKY" ]	[]	[]	[ "" ]	null	[ "Kurt Cagle" ]	2016-02-24T21:40:20+00:00	If you're familiar with my posts here on Linked-In, you're likely aware that I am a strong partisan of MarkLogic. There's good reason for it - overall I think the product is a best of breed in its niche (though that may be a part of the problem it faces), and as a developer, I find that things which	en	https://static.licdn.com/aero-v1/sc/h/al2o9zrvru7aqj8e1x2rzsrca		https://www.linkedin.com/pulse/turning-marklogic-next-oracle-kurt-cagle	If you're familiar with my posts here on Linked-In, you're likely aware that I am a strong partisan of MarkLogic. There's good reason for it - overall I think the product is a best of breed in its niche (though that may be a part of the problem it faces), and as a developer, I find that things which may take me weeks or even months to do on almost any other platform can be accomplished in hours - if you know what you're doing. However, as an analyst, I think there are several things that the company could be doing better. Most of these have come up in conversations with clients, and as such represent the people who are actually using the product for day to day development to manage large projects. Others come from my own observations of the NoSQL industry, and what's working and what's not. Opaque Pricing Structure MarkLogic has some very talented developers on its staff, and they have to be paid, so it is not surprising that the company should command premium prices for its product. However, even after MarkLogic attempt to simplify the pricing structure, it's difficult to know what kind of commitment people are signing up for without in-depth analysis. There's a few things that you could do. One of the easiest would be to put together a pricing tool on the site, keeping in mind that the people coming to get estimates may not necessarily know everything there is to know about the product. This would look at options such as how many clusters would need to be considered, what modules would be enabled, what kind of failover, disaster recovery, hosting solutions and similar configuration issues would need to be set, and so forth. The reality for many organizations (including many of your existing clients) is that when they finally get an eyeful of what it costs to run a fully rigged system, they get sticker shock, and when projects fail (see below) that can make those customers much less inclined to build out MarkLogic in the future. Your competition - Hadoop and Spark, various NoSQL databases and yes, even relational databases - are making inroads because their core product is free, even if the overall development costs can skyrocket pretty quickly. Lack of Product Differentiation MarkLogic is the Swiss Army Knife of database systems. It can handle XML, it can handle JSON, it can do semantics, it can (sort of kind of) do relational. This is great, and one of the reasons that I personally like the product so much. The problem is that no one actually uses Swiss Army Knives, because they have the perception that in trying to make everything work in that small red case, they have to make a lot of design decisions that weaken this functionality. It's also hard to sell that Swiss Army Knife. If you treat MarkLogic as a database, then people will use it as they use any database - a way to store and retrieve XML or JSON. And that's fine, if somewhat overkill. The problem that I see with MarkLogic is that it's not just a database - it's actually a very sophisticated application development platform, yet most people are unaware of the horsepower underneath the hood. A good solution here would be to start the process of differentiation. Companies who are using it for Metadata Management will have different requirements from someone working with a Smart Data Hub. Others will be looking for a big data analytics platform that can tie in with their Hadoop or relational databases. Some people are simply looking for ways of authoring web sites or portals. What this says to me is that each of these are applications, and they are worth quite a bit to your costumers who want to solve real world problems rather than spend all their time trying to make a black box data system fetch and beg. What may be worth considering is taking advantage of your position to build out different configurations that can be sold either as stand-alone applications or starter kits. This may require making some hard decisions with regard to your secondary vendor community, because you will be competing directly with them, but there is a real danger that in doing so, you're endangering your own long term viability. Consider creating a Site Builder app, an analytics app, a Smart Hub, a Metadata Management System, a semantic news portal and a standardized CMS application. Each of these are scripts and services that would sit on top of MarkLogic, that could be installed directly from an admin dashboard, and that would get people functional from day one. You need to have something as easy to use as npm, the package manager from node. Each of these products would be sold independently - yes, it may be the same code underneath, but the application is what most people will see. This turns MarkLogic server into a platform. Going Open Source Intelligently This next one's tough, but increasingly I think it would nail MarkLogic's adoption. Release the server, unadorned with language packs extended analytics toolkits, or similar "advanced" features, for a nominal amount - free, to perhaps $1000. Licensed cluster. Include the website starter kit. Make it available to hosted services, make it an inexpensive option on Amazon, and so forth. You're almost there now - a person with a developer license could essentially use the server now, but there's a lot of ambiguity about whether it is legal to do so. With a clear community license, you will get this product into colleges, small businesses, state organizations, pretty much everywhere. This will establish a developer community that will in turn create their own product ecosystem. You'd still be selling multiple cluster systems as well as the aforementioned targeted packs, but this gets the product in the door. Such a community license would allow for a three node cluster, which is essentially the rational minimal node configuration. Will this impact revenue? Initially, yes, it likely will. However, longer term, it puts MarkLogic into a position to dominate the NoSQL market in terms of overall servers deployed. Your competitors (and yes, those NoSQL vendors are most certainly your competitors, even if they're not acknowledged as such) will eventually dominate the market otherwise, making ancillary sales when you're not. The SQL Option Another area where I relatively small investment could have huge dividends would is in your SQL solution. Yes, MarkLogic has a SQL solution, using indexes tied into XML structures to represent content. There are three key problems with it. First, XML does not always cleanly translate into SQL because their graph models are different (normalization and referencing gets complex). The second, the solution that you currently have is difficult to configure and has unexpected side effects, especially around outer joins. However, the biggest problem is simply that the SQL you have is read-only, and that's a non-starter for almost any organization. What I would recommend instead is that you turn to the semantic triple store and a W3C standard I've mentioned on Linked-In before - R2RML. This standard provides an RDF encoding of a relational database within a triple store. With it, you should be able to use an ODBC bridge to read a database and reconstruct that on MarkLogic. The principle challenge then would be to write a SQL to SPARQL converter and optimizer to let people both query and write to the RDF store completely transparently. From the outside, MarkLogic would simply become another relational database, but from the inside it can be queried either via SQL or SPARQL. In a similar vein, it may be worth packaging a set of SQL drivers for communicating with Microsoft, Oracle, Postgres, Cassandra, etc., in your XQuery and Javascript API. As a recommendation, build out an administration interface that would let you set up multiple connection points, then a developer would only needed to call a named connection point and start writing SQL. While there are a lot of good reasons for doing this, the principle business case is analytics. The analytics space right now is still very SQL oriented, and likely will be so for the foreseeable future, and creating separate indexes on XML objects is simply not efficient. Additionally, there's a lesson that can be learned from the Hadoop build-out. Hadoop's biggest selling point is that it can create SQL-like stores - 90% of all Hadoop projects essentially are dedicated to creating large scale integrated RDB systems (which is ironic, because it's actually a pretty poor use for the technology). MarkLogic flirted with Hadoop but mainly through HDFS. MarkLogic as an RDB store (that was also an RDF store!) would make MarkLogic competitive with Hortonworks or Cloudera in an area where they are actually fairly weak. Make Better Use of Your Admin Layer The MarkLogic Content Pump (MLCP) is a great feature, but it suffers from configuration hell. Because it sits outside of the server proper, importing or exporting content can become a day-long exercise, with no real way to make use of these scripts from within the system. There are a number of processes like this - setting up namespace prefix associations for SPARQL comes to mind as one of the more common, or running a package manager in a consistent way. There are several tasks that are like this. Personally, I think that one of the most powerful tools that MarkLogic could create would be a way to add new panes into the admin environment dynamically. There was a move to make the admin tools available as an API a few years back, now it may be time to take the opposite approach, to provide a toolset that lets developers create their own admin screens that could tie into the ones on port 8001. For instance, suppose that I (as a vendor) have created a new application, and I wanted to set configurations to customize that application. The most logical place to put that configuration would be in the admin section, rather than having to devote a lot of time to building out a separate admin layer as part of the application itself. Stop Trying To Be Oracle Aiming to be the next Oracle is an impressive goal. It also misses the point. Oracle came of age at a time when most of the databases out there were very primitive, and brought relational algebra to the market in a way that revolutionized that industry. Not surprisingly, they soon came to dominate the market, and as they did so, they stopped being an innovator and shifted over to being an aggregator. The result of this were thousands of different products, many of which were only nominally compatible with one another (they had the same logo on the outside, typically), with most of those products spilling very specialized niches. Some of their technology is still awe-inspiring today, but a lot of it is complex, hard to use and of dubious value to the business process. They are now a very conservative force in the industry, and while they continue to maintain large contracts with many organizations and agencies, they are conspicuously absent in the next generation of companies. I like MarkLogic precisely for the fact that it is the first enterprise level data-server that I've seen that manages to marry object, relational and graph data systems into a single integrated unit. With a bit of tweaking, it could become the first true data virtualization platform on the planet. And it is a platform, not just a database. It has to be. Data virtualization is complex, has requirements that move beyond simply storing and retrieving data (most data virtualization occurs at the transformational level), and has a fairly stiff performance requirement in order to do well. MarkLogic can meet these requirements easily, when most data systems require lots of expensive customization just to get to the stage where MarkLogic is out of the box. Now, will MarkLogic ever hit Oracle's sales numbers? At Oracle's peak, it easily dominated a market where its customers were in need of any kind of data access capability. Today the market is over-saturated with just about every kind of data storage you could name, though the integration problems persist. However, I think it likely that at some point in the not too distant future MarkLogic will exceed Oracle's annual sales revenue. Most companies are now spending more money on analysts (under the rubric of data scientists) than they are on data programmers. By making MarkLogic indispensable to those data scientists (and the managers who rely upon them for their interpretations), you have the opportunity to shape the Smart Data market, but to do that, MarkLogic needs to expand its tools in that realm, rather than simply being content to be the platform on which others build. You cannot define a market by trying to be like the market leader. Other Than That, I'm Good MarkLogic does a great deal right, without making it look hard. It's one of the things I like about the company. Yet it needs to move beyond being a black box and instead needs to start thinking of itself as a platform company. The Java and RESTful services, while useful to reach a core developer market, actually work against this platform approach because they make people see MarkLogic strictly through the lens of being a database. This means investment into building up a new application layer, maybe even spinning off a company that can be dedicated strictly to that task (or keeping the application layer closer to the vest while spinning off ML Professional Services). Overall, I'm optimistic about MarkLogic, and I look forward to the time when it goes public. It's a good product, and is moving towards becoming a great product.
correct_foundationPlace_00033	FactBench	2	64	https://www.w3.org/2001/sw/wiki/MarkLogic	en	Semantic Web Standards	https://www.w3.org/favicon.ico	https://www.w3.org/favicon.ico	[ "https://www.w3.org/assets/logos/w3c/w3c-no-bars.svg", "https://www.w3.org/2001/sw/wiki/resources/assets/poweredby_mediawiki_88x31.png" ]	[]	[]	[ "" ]	null	[]	null		en	/favicon.ico		null	MarkLogic Name of the tool: MarkLogic Home page: http://marklogic.com Date of latest release: Programming language(s) that can be used with the tool: Java, JavaScript, XQuery Relevant semantic web technologies: RDF, SPARQL Categories: Triple Store, Programming Environment See also: http://docs.marklogic.com/guide/semantics/semantic-searches Public mailing list: Preferred project URI: DOAP reference: Company or institution: MarkLogic (Tool description last modified on 2016-02-13.) Description
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"https://blog.nashtechglobal.com/wp-content/uploads/2023/04/cmmi5-logo-473BEF2C9F-seeklogo-1.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-27001.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-27002.svg", "https://blog.nashtechglobal.com/wp-content/uploads/2023/04/ISO-9001.svg" ]	[]	[]	[ "" ]	null	[ "Khalid Ahmed" ]	2022-09-29T04:00:00+00:00	Marklogic, Formally known as MarkLogic Server, is an enterprise NoSQL database with broad support for unstructured and unstructured data, including JSON, XML, RDF, text, and binary data types. MarkLogic has schema flexibility, High Scalability, high availability of a NoSQL database, and enterprise features. Before starting with how to make the communication let’s understand the MarkLogic […]	en	https://i0.wp.com/blog.n…it=32%2C32&ssl=1	NashTech Insights	https://blog.nashtechglobal.com/how-to-setup-create-a-database-and-communicate-with-marklogic/	Marklogic, Formally known as MarkLogic Server, is an enterprise NoSQL database with broad support for unstructured and unstructured data, including JSON, XML, RDF, text, and binary data types. MarkLogic has schema flexibility, High Scalability, high availability of a NoSQL database, and enterprise features. Before starting with how to make the communication let’s understand the MarkLogic system requirement. Christopher Lindblad founded MarkLogic in 2001, particularly in handling queries over multi-terabyte document collection. We can install Marklogic on different operating systems: Windows 7 (x64) and 8 (x64) Linux Mac OS Microsoft Windows Server 2012 or 2008, etc. For RAM, MarkLogic requires a minimum of 512 MB of system memory. For the disk space, MarkLogic requires 1.5 times the size of the loaded source content to allow for merging. After installing the MarkLogic from https://developer.marklogic.com/products/marklogic-server?d=/download/binaries/10.0/MarkLogic-10.0-9.4.x86_64.rpm and after setting up you can efficiently run it on your system by running the command /sbin/service MarkLogic start . Now the MarkLogic is installed and the service has been started. The next step is we have to initialize the instance. Basically, you need to set up the user name and the password for the administrative user to set up security. When you are done with all the above steps then you get this type of screen shown below picture where you need to enter the username and password that you already setup before entering in MarkLogic – After the above step, your server gets started and you find out the below window: Creating a Forest and Database As now our instance is set up before communicating we need to create the database first. In MarkLogic database is a set of configurations or we can say that it is a collection of forests. Now the question arises of what exactly the forest means here. So basically the forest is something where the documents in a database are physically stored. Now let’s see how to create a forest in Marklogic. You need to click on the forest section first and then go to the create section that is shown in the below window. Here you just need to fill in the forest name the only mandatory thing. Now, if you want to check whether your new forest is created or not you just need to run ls /var/opt/MarkLogic/Forests in the command prompt. Now we need to create a database by choosing database and then choosing the create option which is shown : After clicking on ok, get a notification where it informs you that your database is not attached to the forest. We need to attach the database to the forest now. Just click on the Database -> Forests which is shown in the above picture. After clicking on it you are going to get a new window for the attachment. Then select the option attached and click ok. After this, you can check the status of whether your database is attached to the forest or not. You need to click on the database name that you created which is present on the left side of the server. At last, our database is created successfully. Communicating with MarkLogic Now we need to communicate with MarkLogic so we have different administrative as well as development interfaces to communicate. Let’s elaborate on some of the administrative interfaces and development interfaces: Admin Interface: It is implemented as a MarkLogic Server web application. By default, it runs on port 8001 of your hosts. Also, it provides a GUI for a multitude of administrative tasks such as creating and configuring databases, forests, security, etc. Admin API: Modifies MarkLogic Server configuration files that are stored on the file system, not in a database. Configuration Manager: It allows you to view the configuration settings for MarkLogic Server resources. Monitoring Dashboard: It provides task-based views of MarkLogic Server performance metrics in real-time. Query Console: It is an interface that allows the developers to communicate with MarkLogic by writing and testing the queries. XQuery (XML Query): A query and functional programming language that queries and transforms collections of structured and unstructured data. It’s a native programming language in MarkLogic. JavaScript is included in the common development interface. It is also a native programming language in MarkLogic. REST API a common development interface provides a programming language for developers to use to communicate with the MarkLogic. Using the Query Console Query console interface readily available on port 8000(http://localhost:8000/qconsole/) and allow us to execute the XQuery and JS expressions easily. Now if you want to execute a query then you need to select the database from the dropdown. If you want to explore or edit the database then you can do it by just clicking on explore. You can also select the app server and set the query language from the dropdown. On the right side, the active workspace is also present. Now after selecting everything our query editor is ready to work. After writing the query you can run it by clicking on the run button successfully and you get the result. There is a feature present in the run environment that you can change the format for query result display. You can use one more feature which is the editor option. The editor options enable you to configure the auto-close functions for parenthesis using auto-complete. You can also control indenting, matching brackets, and closing brackets. Now setting up everything you can write queries in different languages by selecting the query type in which you are comfortable. Conclusion In this blog, we learn how we can set up the database, and for the database, the forest plays an important role. Also, Marklogic provides different query types so developers can choose their suitable type. MarkLogic is most uniquely useful for querying over massive document stores. Reference: https://www.youtube.com/watch?v=60zIQ-6xJ1I https://docs.marklogic.com/guide/qconsole/intro#id_21323 https://docs.marklogic.com/guide/qconsole/walkthru#id_43686 https://www.youtube.com/watch?v=k8F5IFC_sic https://en.wikipedia.org/wiki/MarkLogic
correct_foundationPlace_00033	FactBench	1	8	https://www.dataversity.net/enterprise-nosql-drives-synthesized-meaningful-data-next-gen-apps-processes/	en	MarkLogic's New Enterprise NoSQL Solution Drives Next	https://d3an9kf42ylj3p.c…z_ml9_052916.png	https://d3an9kf42ylj3p.c…z_ml9_052916.png	[ "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://dv-website.s3.amazonaws.com/uploads/2016/05/jz_ml9_052916.png", "https://dv-website.s3.amazonaws.com/uploads/2016/05/jz_ml9_052916.png", "https://d3an9kf42ylj3p.cloudfront.net/uploads/2024/01/1x1.png", "https://d3an9kf42ylj3p.cloudfront.net/uploads/2024/01/1x1.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://www.dataversity.net/wp-content/themes/dataversity/inc/images/dv-logo.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Twitter_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Twitter_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Linkedin_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Linkedin_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Youtube_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Youtube_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Flipboard_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Flipboard_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Facebook_white.png", "https://dv-website.s3.amazonaws.com/uploads/2022/03/Facebook_white.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.26-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.26-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.16-PM.png", "https://dv-website.s3.amazonaws.com/uploads/2018/12/Screen-Shot-2018-12-26-at-2.44.16-PM.png" ]	[]	[]	[ "" ]	null	[ "Jennifer Zaino" ]	2016-06-02T07:30:48+00:00	Enterprise NoSQL database provider MarkLogic has been pushing down that path by helping organizations integrate their data – structured and unstructured – in one place with its schema-agnostic data model for some time now. It’s possible to load data as is into the system and use a universal index to get at it.	en	/wp-content/uploads/2015/10/DV-R-1025-Transparent.png?x38402	DATAVERSITY	https://www.dataversity.net/enterprise-nosql-drives-synthesized-meaningful-data-next-gen-apps-processes/	It’s time for the enterprise to seize the opportunity to build new applications and processes based on a synthesis of meaningful data brought together from diverse systems. Doing so requires some key things, though, starting with a database platform that supports seamlessly integrating the data and ensuring that it is understandable at the conceptual level. Enterprise NoSQL database provider MarkLogic has been pushing down that path by helping organizations integrate their data – structured and unstructured – in one place with its schema-agnostic data model for some time now. It’s possible to load data as is into the system and use a universal index to get at it. “There’s been so much talk over the past years about Big Data. But in the enterprise space there’s often not even the opportunity to have Big Data, because data is broken into so many different systems, applications, and silos that they can’t bring it together,” says Joe Pasqua, MarkLogic EVP of Products. “We want to bring it together not just for data warehouse or analytics but to operate on.” The MarkLogic database also has long had in place a semantic foundation. It acts not only as a document store for storing JSON and XML, but offers an integrated triple store for storing RDF triples that can be linked together to describe facts and relationships. Now, MarkLogic is preparing to take things to the next level, with a platform for building next-generation applications with MarkLogic 9 that previewed in early May of 2016, and is due to ship by year’s end. The latest version, says Pasqua, is going to further drive the possibilities for a database to do smart things to help build next-gen applications, rather than just serve as a dumb repository of data. “If you’re not going to tell the database anything about the data, then there’s only a limited set of things the database has the opportunity to do,” he says. Building up Database Smarts MarkLogic 9 changes the equation, building on the semantic foundation it has in place to provide new capabilities such as Entity Services, which let developers give their data consistent meaning using a semantic model of the key concepts and the relationships between them. It’s a way to provide a high-level concept of business entities vs. a detailed low-level description at the physical level, and to let databases do something for developers that they didn’t have a chance to do before. “We store that information, version it, and make it available to apps in a consistent way, but also to the database to get smart about things,” he says. This can lead to automatically creating REST APIs for sharing customer entities, product entities, supplier entities, and so on. “It’s important because of the world of increasing micro-services,” he says, such complex applications are composed of small, independent processes communicating with each other via APIs. “You need an architecture that directly and natively supports that.” Another new feature is the Optic API query mechanism. As a document-oriented NoSQL database, it’s natural to query information as documents, Pasqua says, but sometimes developers want to see tabular data. “This lets you look through a tabular lens and see data in tabular form and do rollups and aggregates on it,” he says. Alternately, it lets users see data through a semantic lens or even see semantic data through a tabular lens. “It lets you have the most natural way of looking at data depending on what you are trying to achieve,” he says. Hidden from the developer are underlying technologies: A new index and distributed execution across a cluster for fast and effective performance. The SQL capabilities present in this enterprise NoSQL database are enhanced too, for integrating data from MarkLogic with existing SQL tools. “Folks have tools like Tableau that use ODBC [Open Database Connectivity] to get at data and we must provide a bridge so customers can use the tools they depend on and to get value out of MarkLogic at the same time,” he says. Enterprises are in a transition period, he notes, and there’s got to be a connection so that people can get their jobs done today but also move forward. “Our big challenge to ourselves is how to give them better tools to deal with what they have got, but also to allow them to move onto the next generation,” says Pasqua. Continuing Focus on Security The last thing a CIO wants to see on the front pages of newspapers and websites is a headline screaming that his or her company’s data has been breached, and that’s a big reason MarkLogic has always taken security seriously, as has its use for sensitive government applications, Pasqua says. In the security realm, it’s been distinguished as a Common Criteria-certified NoSQL database, for instance. Today, the market generally is looking to focus more tightly on security for the enterprise. For MarkLogic 9, that means a few things, starting with adding advanced encryption capabilities to deal with outsider and insider threats. Encryption technologies will reside in the core of the database, and even system administrators with root access to the system won’t be able to see encrypted data, for instance: “Advanced key management capabilities to keep things safe, along with fine-grained controls over what even administrators can do with the database from a security perspective, are very important to customers,” he says. Redaction features are part of the picture, too. “The idea is that part of the goal of bringing data from different systems is to make that data valuable to more people, so you want to give them access but you may need to redact certain elements of the data depending on who uses it,” he says. For example, a healthcare organization may want researchers to get their hands on data that can be highly valuable for researching disease treatments, but certainly they don’t want those researchers to have access to patients’ personally identifiable information (PII). With MarkLogic 9, the PII can either be removed or randomized. There are plenty of other scenarios where that capability has additional value in large enterprise environments, too. For instance the best testing for QA environments happens when the data used reflects what’s really going on in the production system. But of course businesses don’t want real, sensitive data just floating around in those environments. “Redaction lets you take the data out of production, redact it and put it right back into the QA environment,” he says. MarkLogic 9 also is adding to the role-based security it has incorporated at the document level by adding the same at the element level. So, an individual document can have elements in it that are top secret, for example, and others that are merely secret. “Depending on the clearance level of the people querying information, they will only see the information they are allowed to see in a single document,” he says. MarkLogic has an advantage in that it doesn’t have to bolt on security to its solution as some other enterprise NoSQL products might, since the company always has had an enterprise focus. Generally speaking, Pasqua says, NoSQL started out being used in places and for tasks where security was not a paramount issue. “The challenge is that once you build a system, it’s hard to go back and get security into it, versus building the fundamentals of it into it from the beginning,” he says. “Obviously it’s of paramount importance, though, and doing it right is the challenge.” Manageability Matters and So Does the Cloud Pasqua also points to manageability as a critical issue as more data comes together, systems get bigger and replication expands across geographies. So MarkLogic has created an Ops Director single pane of glass to view an organization’s entire MarkLogic infrastructure and manage it uniformly. A Rolling Upgrade feature was created in the service of non-disruptive operations, so that new versions can be installed on one machine in a cluster while the application stays running elsewhere, with the installation then rolling through the cluster, so that customers don’t have to experience downtime. The company also is mindful of the growing prominence of the Cloud in the enterprise: MarkLogic already is in the Amazon marketplace and runs on Microsoft Azure and Google Cloud. While some features in the latest release aren’t Cloud-specific, they are Cloud-relevant, he notes. For example, its encryption enhancements could help ease the concerns of customers about taking their data to the Cloud and having external service provider administrators supporting those systems, he says. Enhancements to MarkLogic 9’s tiered storage usage capabilities also “make it smarter about the way it uses storage tiers and how they are queried, and that makes it more effective for enterprises to use the cloud cost effectively,” he says. MarkLogic already has begun an early access program for MarkLogic 9, which it will be expanding. “We like to do that because it lets customers give us feedback while in the development process,” says Pasqua, “and it’s good for them because they can start building next-generation apps with new features now.”
correct_foundationPlace_00033	FactBench	2	2	https://docs.marklogic.com/guide/concepts/overview	en	Overview of MarkLogic Server (Concepts Guide) — MarkLogic Server 11.0 Product Documentation			[ "https://docs.marklogic.com/images/ML-Logo-1.png", "https://docs.marklogic.com/images/i_pdf.png", "https://docs.marklogic.com/apidoc/images/printerFriendly.png" ]	[]	[]	[ "marklogic", "enterprise nosql database", "enterprise nosql", "database", "nosql", "nosql database", "nosql db", "xml", "xml database", "json", "enterprise", "bigdata", "big data", "xquery", "xslt", "petabyte", "java db", "java database", "content", "content store", "content database", "content db", "content management system", "CMS", "document", "document-oriented databases", "document database", "document db", "document store", "DB", "xml database", "xml db", "json db", "nonrelational", "nonrelational database", "nonrelational db" ]	null	[]	null	MarkLogic is the only Enterprise NoSQL Database	en			null	Overview of MarkLogic Server MarkLogic is a database designed from the ground up to make massive quantities of heterogenous data easily accessible through search. The design philosophy behind the evolution of MarkLogic is that storing data is only part of the solution. The data must also be quickly and easily retrieved and presented in a way that makes sense to different types of users. Additionally, the data must be reliably maintained by an enterprise grade, scalable software solution that runs on commodity hardware. The purpose of this guide is to describe the mechanisms in MarkLogic that are used to achieve these objectives. MarkLogic fuses together database internals, search-style indexing, and application server behaviors into a unified system. It uses XML and JSON documents as its data model, and stores the documents within a transactional repository. It indexes the words and values from each of the loaded documents, as well as the document structure. And, because of its unique Universal Index, MarkLogic does not require advance knowledge of the document structure and adherence to a particular schema. Through its application server capabilities, it is programmable and extensible. MarkLogic clusters on commodity hardware using a shared-nothing architecture and supports massive scale, high-availability, and very high performance. Customer deployments have scaled to hundreds of terabytes of source data while maintaining sub-second query response time. This chapter contains the following topics:
correct_foundationPlace_00033	FactBench	1	30	https://www.businesswire.com/news/home/20110322005599/en/World-Class-Organizations-to-Share-Stories-of-Innovation-and-Success-at-MarkLogic-2011-User-Conference	en	World Class Organizations to Share Stories of Innovation and Success at MarkLogic 2011 User Conference	https://mms.businesswire.com/bwapps/mediaserver/ViewMedia?mgid=238010&vid=2	https://mms.businesswire.com/bwapps/mediaserver/ViewMedia?mgid=238010&vid=2	[ "https://www.businesswire.com/images/bwlogo_extreme.png", "https://mms.businesswire.com/bwapps/mediaserver/ViewMedia?mgid=238010&vid=2", "https://www.businesswire.com/images/icons/icon_search.gif", "https://www.businesswire.com/images/icons/icon-close-default.svg" ]	[]	[]	[ "" ]	null	[]	2011-03-22T10:00:00+00:00	MarkLogic today announced that Raytheon, McGraw-Hill, Bank of America Merrill Lynch, and other customers, will speak at the MarkLogic 2011 User Confer	en			https://www.businesswire.com/news/home/20110322005599/en/World-Class-Organizations-to-Share-Stories-of-Innovation-and-Success-at-MarkLogic-2011-User-Conference	SAN CARLOS, Calif.--(BUSINESS WIRE)--Successful organizations understand that leveraging unstructured information can significantly impact company agility and drive them to invent new products. MarkLogic® Corporation, the company revolutionizing the way organizations leverage unstructured information, today announced that representatives from Raytheon, McGraw-Hill, Bank of America Merrill Lynch, and others, will join to discuss these stories of success at the MarkLogic 2011 User Conference. For more information or to register for the summit, taking place April 26-29 at the Palace Hotel in San Francisco, CA, please visit http://www.marklogicevents.com/. “World class organizations are using MarkLogic in cutting edge technology implementations,” said Tracy Eiler, senior vice president of marketing, MarkLogic. “The 2011 MarkLogic User Conference offers the opportunity to hear first-hand how these companies are using unstructured information to create value and increase agility. In a big data world, our customers have implemented solutions to leverage unpredictable and complex data to give them a competitive edge – something that has been nearly impossible with relational databases.” Highlights of the agenda include: Bank of America Merrill Lynch – Rupert Brown, Bank of America Merrill Lynch, will discuss how the frenzy and hype around cloud computing has ignored the fact that most large enterprises cannot begin to migrate their mission critical systems to more agile platforms. The session will examine the challenge of untangling the spaghetti of integration technologies and content flows. It will continue to look at the approach Bank of America and MarkLogic are using to address this problem in the context of ensuring the correct information is being delivered to key systems. Raytheon – Bruce Bumgarner, Raytheon, will discuss how the Department of Defense and intelligence communities have implemented a key cross-agency information sharing component on MarkLogic. The session will look at the testing and scaling the MarkLogic system meets to ingest and query large loads in a real application. McGraw-Hill – Richard Fusco, McGraw-Hill, will present a case study on using MarkLogic as the foundation for new content applications and product development. Fusco will discuss how McGraw-Hill used MarkLogic to quickly create prototypes and new products, develop mobile applications and integrate semantic search. The conference sponsors include platinum sponsor Avalon Consulting; gold sponsors Cognizant, Innodata Isogen, Infosys, and Virtusa; silver sponsors iFactory, Janya, Antenna House, TEMIS, HTC Global Services, Applied Relevance, Typefi, and Data Conversion Laboratory, Inc.; bronze sponsors RSuite and Really Strategies; and additional sponsors Flatiron Solutions, ISYS, and Planman. To register or learn more about the MarkLogic User Conference in San Francisco, CA, at the Palace Hotel on April 26-29, please visit http://www.marklogicevents.com/. Submissions are being accepted for the MarkLogic User Conference Awards and the entry form can be found here. The deadline to apply is Friday, March 25. Attending the event? Let us know on Facebook and LinkedIn. The hashtag for the event is #MLUC11. About MarkLogic Corporation MarkLogic is revolutionizing the way organizations leverage information. The company’s flagship product is a purpose-built database for unstructured information. Customers in industries including media, government and financial services use MarkLogic to develop and deploy information applications at a fraction of the time and cost as compared to conventional technologies such as relational databases and search engines. MarkLogic is headquartered in Silicon Valley with field offices in Austin, Boston, Frankfurt, London, New York, and Washington DC. The company is privately held with investors Sequoia Capital and Tenaya Capital. For more information or to download a trial version, go to www.marklogic.com. Copyright © 2011 MarkLogic Corporation. All Rights Reserved. MARKLOGIC® is a registered trademark of MarkLogic Corporation. All other trademarks mentioned herein are the property of their respective owners.
correct_foundationPlace_00033	FactBench	0	0	https://git.cs.uni-paderborn.de/sheid/FactExtract/-/blob/master/train.tsv%3Fref_type%3Dheads	en	Files · master · Stefan Heid	https://git.cs.uni-pader…1570febab5d2.jpg	https://git.cs.uni-pader…1570febab5d2.jpg	[ "https://git.cs.uni-paderborn.de/uploads/-/system/appearance/header_logo/1/irb-logo_gitlab_scaled.png" ]	[]	[]	[ "" ]	null	[]	null	Miniproject for SNLP - Responsible Tutor is Ricardo Usbeck	en	/uploads/-/system/appearance/favicon/1/logo_neu_giticon.ico	GitLab	https://git.cs.uni-paderborn.de/sheid/FactExtract/-/tree/master	"train.tsv?ref_type=heads" did not exist on "master"
correct_foundationPlace_00033	FactBench	2	65	https://www.linkedin.com/pulse/what-marklogic-timo-meijrink	en	What is it exactly that MarkLogic does?	https://media.licdn.com/dms/image/C4E12AQEaznXUbulnbQ/article-cover_image-shrink_600_2000/0/1578301117424?e=2147483647&v=beta&t=-ZR9OsPuh_BPiLOxxasKkegR2Pr6L0kHDVeep9wIwNI	https://media.licdn.com/dms/image/C4E12AQEaznXUbulnbQ/article-cover_image-shrink_600_2000/0/1578301117424?e=2147483647&v=beta&t=-ZR9OsPuh_BPiLOxxasKkegR2Pr6L0kHDVeep9wIwNI	[ "https://media.licdn.com/dms/image/C4E12AQEaznXUbulnbQ/article-cover_image-shrink_600_2000/0/1578301117424?e=2147483647&v=beta&t=-ZR9OsPuh_BPiLOxxasKkegR2Pr6L0kHDVeep9wIwNI", "https://media.licdn.com/dms/image/C4E12AQHgBFcXz56CuA/article-inline_image-shrink_1000_1488/0/1578300442126?e=1727308800&v=beta&t=S9I7kf4ccJO5QSjnYNwBOvANTfHTIHjiuo39-OgaXsI", "https://media.licdn.com/dms/image/C4E12AQEB3NkLx-6rhQ/article-inline_image-shrink_1000_1488/0/1578300489080?e=1727308800&v=beta&t=annQ5hEivkfIo6qBpn4LtxIYdJ1p8nQZ4_u4o7OKqVc" ]	[]	[]	[ "" ]	null	[ "Timo Meijrink" ]	2020-01-06T09:00:49+00:00	This article is part of a series, the other parts can be found at the bottom of the article An introduction to MarkLogic, unstructured agile information management It has been half a year since I started at MarkLogic and during that period the question I got asked the most from friends and family is	en	https://static.licdn.com/aero-v1/sc/h/al2o9zrvru7aqj8e1x2rzsrca		https://www.linkedin.com/pulse/what-marklogic-timo-meijrink	This article is part of a series, the other parts can be found at the bottom of the article An introduction to MarkLogic, unstructured agile information management It has been half a year since I started at MarkLogic and during that period the question I got asked the most from friends and family is; "what is it exactly that MarkLogic does?". MarkLogic can be a lot of things and every time I found myself taking a different avenue when explaining it, not really adding to the clarity about what it is that MarkLogic does. Therefor I decided to write a couple of short articles explaining what it is that MarkLogic does, what makes it unique and what makes me such a fan. To help the people around me understand what MarkLogic is and maybe also a little bit to organize my own thoughts around what it is that I see in MarkLogic. After putting this to paper and rewriting and re-structuring it - a lot -, I ended up with comparing several concepts that exist in, amongst other places, information management. Concepts like Unstructured vs Relational, Agile vs Waterfall and All-in-one vs Best-of-breed. Once it is clear where MarkLogic stands in all of these, I have created a little extra context around the key concepts in MarkLogic, security, multi-model, semantics, metadata, applications services, indexing and search. What is it? MarkLogic is a Not only SQL Enterprise Multi Model Database. I am smiling while writing this because I know many people will read this and think about throwing in the towel on this entire article. But trust me, at the end of the series, you will see the same sentence and understand exactly what it means. To understand what MarkLogic is we have to go back to when it all began. All the way back to 2001 when terrorist attacks in the US laid bare an information sharing issue in US intelligence. After 9/11 the US government tasked the intelligence community to find a better way to share information. Several great minds were approached, amongst them MarkLogic founder Christopher Lindblad, at that time working for one of the early internet search engines. Christopher believed that the solution was a combination of a search engine, a database for unstructured information and an application server. His employer at that time did not want to pursue it, therefor Christopher decided to go out on his own and build it. Now this is where most people I am trying to explain MarkLogic to, start zoning out, because I have lost them at the “database for unstructured information” part, so let's start with that. Grocery shopping (Unstructured vs Relational) It is a Saturday afternoon and you receive a message in one of your Whatsapp groups with friends, "anybody up for hanging out tonight?". That is perfect, you had nothing else planned and you would have probably ended up just watching some Louis Theroux documentaries in your onesie, so you welcome the opportunity to mingle with a fun group of people. You respond right away "YES! 20:00 at my place!!". The moment you hit send, you realize that your quiet afternoon of doing nothing is now also shot. Your fridge is entirely empty and your friends are going to expect to have a beer when hanging out, so off to the store you go. At the store you head straight to the isle with beers and you are ready to buy some Heineken. Now, like many other brands, Heineken has thought of a very convenient way of carrying your beers home, a beer crate. A typical beer crate has 4 rows and 6 columns (or 6 rows and 4 columns, depending on how you hold it). Each cell in the crate is perfectly dimensioned to hold 1 beer bottle, with just enough space to easily slide in and out but not so much space that they are bouncing around when you transport your beers home. A pretty perfect solution you might think when you look at the purpose it was built for. Having this little internal realization, that you are in the presence of such a great invention, gives you a tingle inside as you start walking towards the register. But then you realize that there will be people that want something else to drink. That means a quick detour through the soda isle. You grab a bottle of coke and a carton of iced tea and put them on top of your beer crate. Look at yourself, creating new purposes for the already brilliant beer crate! While you are at it, you might as well grab some chips and maybe some sweets. Slowly you start creating a pretty big unstable pile on top of your beer crate. While you are balancing your big pile your phone buzzes and with some effort you look at your watch to read what is coming in. 2 Friends seize the opportunity of not having to cook and will come early and stay for dinner. Okay, no worries, another little detour through the isle with frozen pizzas and you grab a couple of those as well. But no way that those will also fit on top of the pile. You slowly start thinking, there must be a better solution than piling all these things on top of your crate, at which point you tilt the crate a little and everything starts moving, before you know it all of your groceries are sliding through the isle… Just when you think there is no more hope, you spot a shopping crate at the checkout. Eureka! You put your soda's, chips, sweets and pizza into the shopping crate and there is even room left to put a 12 pack twist cap Heineken in, that is a more convenient amount of beers and it will save you the trouble of having to return a beer crate next time you go to the store. You say goodbye to the beer crate, thank it for its service and enter into a new life of convenience! That was quite an introduction to explain unstructured versus relational, but we can be really quick about it now. Imagine the beer crate is a relational database, it is a great invention if you want to store information that the relational database is built for. Information that lets itself be put into rows and columns (like an Excel sheet). Real life information however does not come in the form of rows and columns, it comes in the form of documents, emails, texts, relationships, etcetera. You can try putting those on top of your beer crate, but that means you are abusing the beer crate, not using it for the purpose it was built for, leading to bad performance and a lot of bespoke solutioning. Alternatively, you can go back to Heineken and ask them to create a new beer crate that has special boxes for soda, chips, sweets and pizza or go to Lays and ask them to design chips bags in the form of a beer bottle. Heineken and Lays will probably charge you a lot of money and what happens if next time you also need to pick up some garbage bags? This last part is what we see in the database world as an Extract-Transform-Load (ETL) process. It is a costly and inflexible way to change a set of information to fit in the relational model. Now imagine that the shopping crate is an unstructured database, like MarkLogic. Whatever you decide to buy in the grocery store, will fit in the shopping create. You can put all your groceries for today in it and whatever you decide to buy tomorrow. In an unstructured database you load all your real-life information as you have or receive it. This gives you a lot of speed and agility in your information management. How unstructured database came to be I will explain in my next post where I will cover agile vs waterfall information management. The concepts I am discussing in these articles are simplified representations of comprehensive subjects. If you are interested in learning more about specific subjects don't hesitate to reach out! This article is part of a series, you can read whole article here: Marklogic Explained Or read individual chapters here: 1. An introduction to MarkLogic (unstructured agile information management) 2. Plans are useless (Agile vs Waterfall) 3. Swiss army knife (All-in-one vs Best-of-breed) 4. Buying 2nd hand (ACID) 5. Tent lock (Security) 6. Timo-writes_a-blog (Semantics) 7. Power2 (Metadata) 8. The attic (Bi-temporal and geospatial) 9. Who is the enemy? (Vendor lockin v.s. Cloud neutral)
correct_foundationPlace_00033	FactBench	1	67	https://cioinfluence.com/aiops/marklogic-acquires-leading-metadata-management-provider-smartlogic/	en	MarkLogic Acquires Leading Metadata Management Provider Smartlogic	https://itechnologyserie…r-Smartlogic.jpg	https://itechnologyserie…r-Smartlogic.jpg	[ "https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4.png 4163w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-300x86.png 300w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-1024x295.png 1024w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-768x221.png 768w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-1536x443.png 1536w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-2048x590.png 2048w, https://1931032958.rsc.cdn77.org/wp-content/uploads/2017/11/CIO-INFLUENCE-LOGO-4-1920x553.png 1920w, 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integration and portfolio company of Vector Capital, announced it has acquired Smartlogic, a premier metadata management solutions	en	https://1931032958.rsc.c…ng-1-150x150.png	CIO Influence	https://cioinfluence.com/itechnology-series-news/marklogic-acquires-leading-metadata-management-provider-smartlogic/	MarkLogic Corporation, a leader in complex data integration and portfolio company of Vector Capital, announced it has acquired Smartlogic, a premier metadata management solutions provider and leader in semantic AI technology. As part of the transaction, Smartlogic’s founder and Chief Executive Officer, Jeremy Bentley, as well as other members of the senior management team, will join the MarkLogic executive team. Financial terms of the transaction were not disclosed. Founded in 2006, Smartlogic has deciphered, filtered, and connected data for many of the world’s largest organizations to help solve their complex data problems. Global organizations in the energy, healthcare, life sciences, financial services, government and intelligence, media and publishing, and high-tech manufacturing industries rely on Smartlogic’s metadata and AI platform every day to enrich enterprise information with context and meaning, as well as extract critical facts, entities, and relationships to power their businesses. For the past four years, Smartlogic has been recognized as a leader by Gartner’s Magic Quadrant for Metadata Management Solutions and by Info-Tech as the preeminent leader of the Data Quadrant for Metadata Management (May 2021). Top iTechnology Networking News: Nokia Upgrades Guadalajara 5G Lab to Test New Use Cases Jeff Casale, Chief Executive Officer of MarkLogic, said, “Enterprises are facing significantly more complex data challenges than ever before. By acquiring and integrating with Smartlogic, a best-in-class metadata and AI platform, we provide our customers with the tools to more easily unlock the enormous value embedded in human-generated content. We’re very excited to work with Jeremy and his talented team as we grow the business and deliver better outcomes for our customers.” “Smartlogic unlocks the value in important data sets many enterprises rely on by leveraging sophisticated semantic AI to enable better decision making,” said Stephen Goodman, a Principal at Vector Capital. “Smartlogic’s ability to deliver actionable intelligence is complementary with MarkLogic’s powerful offerings and we are excited to deliver a more complete and informed perspective to customers through this combination.” “This is an exciting next step for Smartlogic and I want to thank our entire team for their contributions in reaching this achievement,” said Mr. Bentley. “As part of the MarkLogic family, we will be better positioned to scale our robust AI platform, invest in our market leading technology, and provide an even greater number of customers with a unified data solution that can help solve their most complex business data problems.” Top iTechnology Networking News: Nokia Upgrades Guadalajara 5G Lab to Test New Use Cases
correct_foundationPlace_00033	FactBench	1	71	https://thesiliconreview.com/magazines/the-worlds-best-database-marklogic-integrates-data-4x-faster-than-traditional-approaches	en	The World’s Best Database: MarkLogic Integrates Data 4x Faster than Traditional Approaches	https://thesiliconreview…marklogic-18.jpg	https://thesiliconreview…marklogic-18.jpg	[ "https://thesiliconreview.com/images/WebsitethesiliconreviewLogowhite.png", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-gary-bloom-ceo-president-marklogic-18.jpg", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-reits-invits-gateway-profitable-real-estate.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-digital-mandate-godrej-detsu-creative.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-bitcoin-ethereum-etfs-hong-kong.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-openai-inaugurated-its-first-tokyo-operations-center.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-ph-natl-platform-services-industry-startups.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-global-food-and-beverage-industry-sunil-pande.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-hyundai-ads-x-brand-safety-issues.webp", "https://thesiliconreview.com/story_image_upload/article/thesiliconreview-julius-baer-ceo-thailand-joint-venture.webp", "https://thesiliconreview.com/ads/ad-1.jpg", "https://thesiliconreview.com/ads/ad-2.png", "https://thesiliconreview.com/ads/ad-3.png", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-50-fastest-growing-companies-of-the-year-2024-img.jpg", "https://thesiliconreview.com/TSR.png", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-10-best-women-entrepreneurs-to-watch-2024-cover.jpg", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-50-fastest-growing-companies-of-the-year-2024-img.jpg", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-top-10-companies-to-watch-in-asia-2024-img.jpg", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-5-best-medical-device-companies-to-watch-2024-cover-img.jpg", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-30-fastest-growing-tech-companies-cover-2024.jpg", "https://thesiliconreview.com/story_image_upload/us/thesiliconreview-5-best-iot-companies-5-best-ai-companies-to-watch-2024-img.jpg" ]	[]	[]	[ "" ]	null	[]	null		en	https://thesiliconreview…icon/favicon.png	The Silicon Review	https://thesiliconreview.com/magazines/the-worlds-best-database-marklogic-integrates-data-4x-faster-than-traditional-approaches	MarkLogic is an operational and transactional Enterprise NoSQL database platform trusted by global organizations to integrate their most critical data. Designed to integrate data from silos better, faster, and with less cost, MarkLogic can help you integrate data and build your 360-degree view up to four times faster than if using a traditional database. And, you don’t have to sacrifice any of the enterprise features required for storing and managing mission-critical data. MarkLogic Database Overview: One Database. Endless Possibilities. MarkLogic is a database designed for NoSQL speed and scale, without sacrificing the enterprise features required to run mission-critical, operational applications. Using a multi-model approach, MarkLogic provides unprecedented flexibility to integrate and store all of your most critical data, and then view that data as documents, as a graph, or as relational data. You can avoid expensive and brittle ETL and better manage the entities and relationships that your business works with. Why Multi-Model? To bridge data silos, data management means being able to deal with heterogeneous, multi-shaped, multi-formatted data. Because MarkLogic at its core is a document database, the most flexible of NoSQL databases, it easily loads that multi-shaped data without upfront modeling. Additionally, MarkLogic lets you make associations between documents using triples. So triples, documents, and data — that’s what it means by multi-model. Key Capabilities: Enterprise Ready. Set. Go! Easy to Get Data in: Ingest structured and unstructured data as is with a Flexible Data Model that adapts to both changing data and changing data structure. MarkLogic natively stores JSON, XML, text, geospatial, and semantic triples. Easy to Get Data out: The database has built-in, lightning-fast search capabilities with an “Ask Anything” Universal Index. MarkLogic also provides APIs and other tools to enable fast application development and deployment to any environment. Trusted To Run Your Business: MarkLogic is enterprise-ready, having ACID transactions, scalability and elasticity, certified security, high availability, and disaster recovery — plus other enterprise features required to run critical business operations. MarkLogic Consulting Services: Expert Consulting Within Reach MarkLogic Consulting Services puts an exceptional focus on customer success. With vast experience solving big data challenges for some of the world’s most complex data projects, MarkLogic is committed to helping you scope and select the best application of MarkLogic technology. Its consultants can assist your team throughout projects, at key points of development, or on an as-needed basis. How can MarkLogic Consulting support your needs? Implementation Support: MarkLogic assist with initial critical functionality during development, train and mentor your staff over project iterations, and transition into an advisory capacity over time. Expert Advice: MarkLogic combines formal and informal knowledge-transfer to support technical teams. When you need them most, you can rely on its implementation experts. Full-Service Development: MarkLogic cover all aspects of application development and platform adoption, providing progress updates and eliciting feedback throughout the process. Quick Start Service: 3 Reasons to Start Today Selecting the right software and a partner you can count on is an important decision. Jump-start your project with the Quick Start Service, and build a foundation on which to extend the reach and value of integrating all data sources across your enterprise. Best Technology For over a decade, MarkLogic has delivered a powerful, trusted Enterprise NoSQL (Not Only SQL) database that enables organizations to turn all data into valuable, actionable information. Key features include ACID transactions, horizontal scaling, disaster recovery, high availability, real-time indexing, government-grade security, and more. Expert Support MarkLogic’s Consultants are skilled in configuring, installing, implementing, and integrating MarkLogic technology. They’re educated on the latest product features and the product roadmap for future releases. They work closely with Product Management, Support, and Engineering teams to maintain an exceptional focus on customer success. Low-Risk Approach The MarkLogic Quick Start Service provides a low-risk approach to kick off your innovative, next-generation information application. This program makes it easy for you to evaluate MarkLogic’s capabilities and strengths through a working pilot that’s focused on ensuring repeatable success developing a working application with best-practice architecture. Testimonials “At the start of this project, we called that middle box (the MarkLogic piece) the Conversion Hub because we’re Oracle-based and we wanted to create a place where people could look at the data before it gets ported back over to Oracle. In that process, we got confidence with MarkLogic and said, ‘You know what? We’re grooming this data so let’s stand it up here and let’s write our application around that’ and that became the sales to finance hub with MarkLogic.” Rob Maxwell, Vice President, Worldwide It, Sony Pictures Entertainment “Our legacy system took up to 30 minutes or an hour sometimes [to publish a video clip]. We compared the MarkLogic NoSQL technology to some SQL vendors, and what we got in 20 seconds on SQL took us 200 milliseconds in NoSQL — orders of magnitude faster. So we said let’s move iPlayer to this.” Allan Donald, Senior Product Manager for Editorial Metadata, BBC Greet the Leader Gary Bloom, Chief Executive Officer, and President: Calling upon his extensive executive experience at Oracle and Veritas, Gary Bloom leads MarkLogic as the preeminent NoSQL database for the enterprise. Throughout his 14-year tenure as a senior Oracle executive, Gary helped organizations make the generational shift from mainframe to relational technology – the perfect experience to enable him to assist organizations today in the transformation from relational to next-generation database technology. Gary earned a Bachelors degree in Computer Science from California Polytechnic State University-San Luis Obispo.
correct_foundationPlace_00033	FactBench	2	24	https://docs.datadoghq.com/integrations/marklogic/	en	MarkLogic	https://datadog-docs.img…ils-generic3.png	https://datadog-docs.img…ils-generic3.png	[ "https://datadog-docs.imgix.net/img/dd_logo_n_70x75.png?ch=Width,DPR&fit=max&auto=format&w=70&h=75", "https://datadog-docs.imgix.net/img/dd-logo-n-200.png?ch=Width,DPR&fit=max&auto=format&h=14&auto=format&w=807", "https://datadog-docs.imgix.net/img/datadog_rbg_n_2x.png?fm=png&auto=format&lossless=1", "https://datadog-docs.imgix.net/images/icons/nav_home.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/icons/nav_docs.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/icons/nav_mobile_api.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/os-linux.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/os-macos.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/os-windows-bw.png?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/icons/help-druids.svg", "https://datadog-docs.imgix.net/images/icons/icon-pencil.svg?ch=Width%2cDPR&fit=max&auto=format&w=807", "https://datadog-docs.imgix.net/images/dd-logo-white.svg" ]	[]	[]	[ "" ]	null	[]	null	Datadog, the leading service for cloud-scale monitoring.	en	https://docs.datadoghq.com/favicon.ico	Datadog Infrastructure and Application Monitoring	https://docs.datadoghq.com/integrations/marklogic/	marklogic.databases.average_forest_size (gauge)The average forest size attached to database. Shown as mebibytemarklogic.databases.backup_count (gauge)The maximum number of forests that are backing up. Shown as unitmarklogic.databases.backup_read_load (gauge)Disk read time threads spent for backup, in proportion to the elapsed time.marklogic.databases.backup_read_rate (gauge)The moving average throughput of reading backup data from disk. Shown as mebibytemarklogic.databases.backup_write_load (gauge)Disk writing time threads spent for backups, in proportion to the elapsed time.marklogic.databases.backup_write_rate (gauge)The moving average throughput of writing data for backups. Shown as mebibytemarklogic.databases.compressed_tree_cache_hit_rate (gauge)The average number of hits on the compressed cache. Shown as hitmarklogic.databases.compressed_tree_cache_miss_rate (gauge)The average number of misses on the compressed cache. Shown as missmarklogic.databases.data_size (gauge)The total size of the database on disk. Shown as mebibytemarklogic.databases.database_replication_receive_load (gauge)Time threads spent receiving data for database replication, in proportion to the elapsed time.marklogic.databases.database_replication_receive_rate (gauge)The moving average throughput of receiving data for database replication. Shown as mebibytemarklogic.databases.database_replication_send_load (gauge)Time threads spent sending data for database replication, in proportion to the elapsed time.marklogic.databases.database_replication_send_rate (gauge)The moving average throughput of sending data for database replication. Shown as mebibytemarklogic.databases.deadlock_rate (gauge)The rate of deadlock occurrences. Shown as lockmarklogic.databases.deadlock_wait_load (gauge)Time threads spent waiting for locks that eventually result in deadlocks in proportion to the elasped time.marklogic.databases.device_space (gauge)The amount of space left on the device. Shown as mebibytemarklogic.databases.fast_data_size (gauge)The total size of the fast storage on disk. Shown as mebibytemarklogic.databases.forests_count (gauge)The number of forests for the database. Shown as unitmarklogic.databases.in_memory_size (gauge)The total memory used for the database. Shown as mebibytemarklogic.databases.journal_write_load (gauge)Journal writing time threads spent in proportion to the elapsed time.marklogic.databases.journal_write_rate (gauge)The moving average of writing data to the journal. Shown as mebibytemarklogic.databases.large_binary_cache_hit_rate (gauge)The average number of hits on the large binary cache. Shown as hitmarklogic.databases.large_binary_cache_miss_rate (gauge)The average number of misses on the large binary cache. Shown as missmarklogic.databases.large_data_size (gauge)The total size of the large data on disk. Shown as mebibytemarklogic.databases.large_read_load (gauge)Disk read time threads spent on large documents, in proportion to the elapsed time.marklogic.databases.large_read_rate (gauge)The moving average throughput of reading large documents from disk. Shown as mebibytemarklogic.databases.large_write_load (gauge)Disk write time threads spent for large documents, in proportion to the elapsed time.marklogic.databases.large_write_rate (gauge)The moving average throughput of writing data for large documents. Shown as mebibytemarklogic.databases.largest_forest_size (gauge)The size of largest forest attached to database. Shown as mebibytemarklogic.databases.least_remaining_space_forest (gauge)The lowest free remaining space size. Shown as mebibytemarklogic.databases.list_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.databases.list_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.databases.merge_count (gauge)The maximum number of forests that are merging.marklogic.databases.merge_read_load (gauge)Disk read time threads spent during merge, in proportion to the elapsed time.marklogic.databases.merge_read_rate (gauge)The moving average throughput of reading merge data from disk. Shown as mebibytemarklogic.databases.merge_write_load (gauge)Disk writing time threads spent for merges, in proportion to the elapsed time.marklogic.databases.merge_write_rate (gauge)The moving average throughput of writing data for merges. Shown as mebibytemarklogic.databases.min_capacity (gauge)The least capacity for a forest as a percentage.marklogic.databases.query_read_load (gauge)Disk reading time threads spent for a query in proportion to the elapsed time.marklogic.databases.query_read_rate (gauge)The moving average of throughput reading query data from disk. Shown as mebibytemarklogic.databases.read_lock_hold_load (gauge)Time threads spent holding read locks in proportion to the elapsed time.marklogic.databases.read_lock_rate (gauge)The rate of read lock acquistions. Shown as mebibytemarklogic.databases.read_lock_wait_load (gauge)Time threads spent waiting to acquire read locks in proportion to the elasped time.marklogic.databases.reindex_count (gauge)The total number of reindexing forests for the database.marklogic.databases.restore_count (gauge)The maximum number of forests that are restoring.marklogic.databases.restore_read_load (gauge)Disk read time threads spent for restores, in proportion to the elapsed time.marklogic.databases.restore_read_rate (gauge)The moving average throughput of reading restore data from disk. Shown as mebibytemarklogic.databases.restore_write_load (gauge)Disk write time threads spent for restores, in proportion to the elasped time.marklogic.databases.restore_write_rate (gauge)The moving average throughput of writing data for restores. Shown as mebibytemarklogic.databases.save_write_load (gauge)The moving average of time threads spent writing to in-memory stands, in proportion to the elapsed time.marklogic.databases.save_write_rate (gauge)The moving average of writing data to in-memory stands. Shown as mebibytemarklogic.databases.total_load (gauge)The sum of the processing load factors.marklogic.databases.total_merge_size (gauge)The total size of active forest merging for the database. Shown as mebibytemarklogic.databases.total_rate (gauge)The sum of the processing rate factors.marklogic.databases.triple_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.databases.triple_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.databases.triple_value_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.databases.triple_value_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.databases.write_lock_hold_load (gauge)Time threads spent holding write locks in proportion to the elapsed time.marklogic.databases.write_lock_rate (gauge)The rate of write lock acquistions. Shown as lockmarklogic.databases.write_lock_wait_load (gauge)Time threads spent waiting to acquire write locks in proportion to the elapsed time.marklogic.forests.backup_count (gauge)The maximum number of forests that are backing up. Shown as unitmarklogic.forests.backup_read_load (gauge)Disk read time threads spent for backup, in proportion to the elapsed time.marklogic.forests.backup_read_rate (gauge)The moving average throughput of reading backup data from disk. Shown as mebibytemarklogic.forests.backup_write_load (gauge)Disk writing time threads spent for backups, in proportion to the elapsed time.marklogic.forests.backup_write_rate (gauge)The moving average throughput of writing data for backups. Shown as mebibytemarklogic.forests.compressed_tree_cache_hit_rate (gauge)The average number of hits on the compressed cache. Shown as hitmarklogic.forests.compressed_tree_cache_miss_rate (gauge)The average number of misses on the compressed cache. Shown as missmarklogic.forests.compressed_tree_cache_ratio (gauge)The compressed cache ratio Shown as percentmarklogic.forests.current_foreign_master_cluster (gauge)The cluster ID coupled with the local cluster. Shown as unitmarklogic.forests.current_foreign_master_fsn (gauge)The ID of the last journal frame received from the foreign master Shown as unitmarklogic.forests.current_master_fsn (gauge)The journal frame ID of the local master Shown as unitmarklogic.forests.database_replication_receive_load (gauge)Time threads spent receiving data for database replication, in proportion to the elapsed time.marklogic.forests.database_replication_receive_rate (gauge)The moving average throughput of receiving data for database replication. Shown as mebibytemarklogic.forests.database_replication_send_load (gauge)Time threads spent sending data for database replication, in proportion to the elapsed time.marklogic.forests.database_replication_send_rate (gauge)The moving average throughput of sending data for database replication. Shown as mebibytemarklogic.forests.deadlock_rate (gauge)The rate of deadlock occurrences. Shown as lockmarklogic.forests.deadlock_wait_load (gauge)Time threads spent waiting for locks that eventually result in deadlocks in proportion to the elasped time.marklogic.forests.device_space (gauge)The amount of space left on forest device. Shown as mebibytemarklogic.forests.forest_reserve (gauge)The amount of space needed for merging. Shown as mebibytemarklogic.forests.journal_write_load (gauge)Journal writing time threads spent in proportion to the elapsed time.marklogic.forests.journal_write_rate (gauge)The moving average of writing data to the journal. Shown as mebibytemarklogic.forests.journals_size (gauge)The amount of space the journals take up on disk. Shown as mebibytemarklogic.forests.large_binary_cache_hit_rate (gauge)The average number of hits on the large binary cache. Shown as hitmarklogic.forests.large_binary_cache_hits (gauge)The number of hits on the large binary cache. Shown as hitmarklogic.forests.large_binary_cache_miss_rate (gauge)The average number of misses on the large binary cache. Shown as missmarklogic.forests.large_binary_cache_misses (gauge)The number of misses on the large binary cache. Shown as missmarklogic.forests.large_data_size (gauge)The amount of space large objects take up on disk. Shown as mebibytemarklogic.forests.large_read_load (gauge)Disk read time threads spent on large documents, in proportion to the elapsed time.marklogic.forests.large_read_rate (gauge)The moving average throughput of reading large documents from disk. Shown as mebibytemarklogic.forests.large_write_load (gauge)Disk write time threads spent for large documents, in proportion to the elapsed time.marklogic.forests.large_write_rate (gauge)The moving average throughput of writing data for large documents. Shown as mebibytemarklogic.forests.list_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.forests.list_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.forests.list_cache_ratio (gauge)The list cache ratio Shown as percentmarklogic.forests.max_query_timestamp (gauge)The largest timestamp a query has run at. Shown as millisecondmarklogic.forests.max_stands_per_forest (gauge)The maximum number of stands for a forest. Shown as unitmarklogic.forests.merge_count (gauge)The maximum number of forests that are merging. Shown as unitmarklogic.forests.merge_read_load (gauge)Disk read time threads spent during merge, in proportion to the elapsed time.marklogic.forests.merge_read_rate (gauge)The moving average throughput of reading merge data from disk. Shown as mebibytemarklogic.forests.merge_write_load (gauge)Disk writing time threads spent for merges, in proportion to the elapsed time.marklogic.forests.merge_write_rate (gauge)The moving average throughput of writing data for merges. Shown as mebibytemarklogic.forests.min_capacity (gauge)The least capacity for a forest as a percentage. Shown as percentmarklogic.forests.nonblocking_timestamp (gauge)The most current timestamp for which a query will execute without waiting for transactions to settle. Shown as millisecondmarklogic.forests.orphaned_binaries (gauge)The count of orphaned large binaries. Shown as itemmarklogic.forests.query_read_load (gauge)Disk reading time threads spent for a query in proportion to the elapsed time.marklogic.forests.query_read_rate (gauge)The moving average of throughput reading query data from disk. Shown as mebibytemarklogic.forests.read_lock_hold_load (gauge)Time threads spent holding read locks in proportion to the elapsed time.marklogic.forests.read_lock_rate (gauge)The rate of read lock acquistions. Shown as lockmarklogic.forests.read_lock_wait_load (gauge)Time threads spent waiting to acquire read locks in proportion to the elasped time.marklogic.forests.restore_count (gauge)The maximum number of forests that are restoring. Shown as unitmarklogic.forests.restore_read_load (gauge)Disk read time threads spent for restores, in proportion to the elapsed time.marklogic.forests.restore_read_rate (gauge)The moving average throughput of reading restore data from disk. Shown as mebibytemarklogic.forests.restore_write_load (gauge)Disk write time threads spent for restores, in proportion to the elasped time.marklogic.forests.restore_write_rate (gauge)The moving average throughput of writing data for restores. Shown as mebibytemarklogic.forests.save_write_load (gauge)The moving average of time threads spent writing to in_memory stands, in proportion to the elapsed time.marklogic.forests.save_write_rate (gauge)The moving average of writing data to in_memory stands Shown as mebibytemarklogic.forests.state_not_open (gauge)The number of forests that aren't open. Shown as unitmarklogic.forests.storage.disk_size (gauge)The amount of space the stand takes on disk. Shown as mebibytemarklogic.forests.storage.host.capacity (gauge)The percentage of storage space that is free. Shown as percentmarklogic.forests.storage.host.device_space (gauge)The amount of space left on forest device. Shown as mebibytemarklogic.forests.storage.host.forest_reserve (gauge)The amount of space needed for merging. Shown as mebibytemarklogic.forests.storage.host.forest_size (gauge)The total ordinary storage for forests. Shown as mebibytemarklogic.forests.storage.host.large_data_size (gauge)The amount of space large objects take up on disk. Shown as mebibytemarklogic.forests.storage.host.remaining_space (gauge)The total free storage for forests. Shown as mebibytemarklogic.forests.total_forests (gauge)The total number of forests. Shown as unitmarklogic.forests.total_load (gauge)The sum of the processing load factors.marklogic.forests.total_rate (gauge)The sum of the processing rate factors. Shown as mebibytemarklogic.forests.triple_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.forests.triple_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.forests.triple_value_cache_hit_rate (gauge)The average number of hits on the list cache. Shown as hitmarklogic.forests.triple_value_cache_miss_rate (gauge)The average number of misses on the list cache. Shown as missmarklogic.forests.write_lock_hold_load (gauge)Time threads spent holding write locks in proportion to the elapsed time.marklogic.forests.write_lock_rate (gauge)The rate of write lock acquistions. Shown as lockmarklogic.forests.write_lock_wait_load (gauge)Time threads spent waiting to acquire write locks in proportion to the elapsed time.marklogic.hosts.backup_read_load (gauge)Disk read time threads spent for backup, in proportion to the elapsed time.marklogic.hosts.backup_read_rate (gauge)The moving average throughput of reading backup data from disk. Shown as mebibytemarklogic.hosts.backup_write_load (gauge)Disk writing time threads spent for backups, in proportion to the elapsed time.marklogic.hosts.backup_write_rate (gauge)The moving average throughput of writing data for backups. Shown as mebibytemarklogic.hosts.deadlock_rate (gauge)The rate of deadlock occurrences. Shown as lockmarklogic.hosts.deadlock_wait_load (gauge)The total time spent waiting for locks that eventually deadlocked. Shown as secondmarklogic.hosts.external_binary_read_load (gauge)Disk read time threads spent on external binary documents, in proportion to the elapsed time.marklogic.hosts.external_binary_read_rate (gauge)Disk read throughput of external binary documents. Shown as mebibytemarklogic.hosts.foreign_xdqp_client_receive_load (gauge)Time threads spent receiving data for the foreign xdqp client, in proportion to the elapsed time.marklogic.hosts.foreign_xdqp_client_receive_rate (gauge)The moving average throughput of the foreign xdqp client receiving data. Shown as mebibytemarklogic.hosts.foreign_xdqp_client_send_load (gauge)Time threads spent sending data for the foreign xdqp client, in proportion to the elapsed time.marklogic.hosts.foreign_xdqp_client_send_rate (gauge)The moving average throughput of the foreign xdqp client sending data. Shown as mebibytemarklogic.hosts.foreign_xdqp_server_receive_load (gauge)Time threads spent receiving data for the foreign xdqp server, in proportion to the elapsed time.marklogic.hosts.foreign_xdqp_server_receive_rate (gauge)The moving average throughput of the foreign xdqp server receiving data. Shown as mebibytemarklogic.hosts.foreign_xdqp_server_send_load (gauge)Time threads spent sending data for the foreign xdqp server, in proportion to the elapsed time.marklogic.hosts.foreign_xdqp_server_send_rate (gauge)The moving average throughput of the foreign xdqp server sending data. Shown as mebibytemarklogic.hosts.journal_write_load (gauge)Journal writing time threads spent in proportion to the elapsed time.marklogic.hosts.journal_write_rate (gauge)The moving average of writing data to the journal. Shown as mebibytemarklogic.hosts.large_read_load (gauge)Disk read time threads spent on large documents, in proportion to the elapsed time.marklogic.hosts.large_read_rate (gauge)The moving average throughput of reading large documents from disk. Shown as mebibytemarklogic.hosts.large_write_load (gauge)Disk write time threads spent for large documents, in proportion to the elapsed time.marklogic.hosts.large_write_rate (gauge)The moving average throughput of writing data for large documents. Shown as mebibytemarklogic.hosts.memory_process_huge_pages_size (gauge)The size of huge pages for the MarkLogic process. Available on Linux platform. Sum of Sizes after /anon_hugepage in /proc/[MLpid]/smaps. Shown as mebibytemarklogic.hosts.memory_process_rss (gauge)The size of Process Resident Size (RSS) for the MarkLogic process Shown as mebibytemarklogic.hosts.memory_process_swap_rate (gauge)The swap rate for the MarkLogic process. Shown as pagemarklogic.hosts.memory_size (gauge)The amount of space the stand takes in memory. Shown as mebibytemarklogic.hosts.memory_system_free (gauge)The free system memory. MemFree from /proc/meminfo on Linux, ullAvailPhys from GlobalMemoryStatusEx on Windows. Shown as mebibytemarklogic.hosts.memory_system_pagein_rate (gauge)The page in rate for the system. Shown as pagemarklogic.hosts.memory_system_pageout_rate (gauge)The page out rate for the system. Shown as pagemarklogic.hosts.memory_system_swapin_rate (gauge)The swap in rate for the system. Shown as pagemarklogic.hosts.memory_system_swapout_rate (gauge)The swap out rate for the system. Shown as pagemarklogic.hosts.memory_system_total (gauge)The total system memory. MemTotal from /proc/meminfo on Linux, ullTotalPhys from GlobalMemoryStatusEx on Windows. Shown as mebibytemarklogic.hosts.merge_read_load (gauge)Disk read time threads spent during merge, in proportion to the elapsed time.marklogic.hosts.merge_read_rate (gauge)The moving average throughput of reading merge data from disk. Shown as mebibytemarklogic.hosts.merge_write_load (gauge)Disk writing time threads spent for merges, in proportion to the elapsed time.marklogic.hosts.merge_write_rate (gauge)The moving average throughput of writing data for merges. Shown as mebibytemarklogic.hosts.query_read_load (gauge)Disk reading time threads spent for a query in proportion to the elapsed time.marklogic.hosts.query_read_rate (gauge)The moving average of throughput reading query data from disk. Shown as mebibytemarklogic.hosts.read_lock_hold_load (gauge)Time threads spent holding read locks in proportion to the elapsed time.marklogic.hosts.read_lock_rate (gauge)The rate of read lock acquistions. Shown as lockmarklogic.hosts.read_lock_wait_load (gauge)Time threads spent waiting to acquire read locks in proportion to the elasped time.marklogic.hosts.restore_read_load (gauge)Disk read time threads spent for restores, in proportion to the elapsed time.marklogic.hosts.restore_read_rate (gauge)The moving average throughput of reading restore data from disk. Shown as mebibytemarklogic.hosts.restore_write_load (gauge)Disk write time threads spent for restores, in proportion to the elasped time.marklogic.hosts.restore_write_rate (gauge)The moving average throughput of writing data for restores. Shown as mebibytemarklogic.hosts.save_write_load (gauge)The moving average of time threads spent writing to in-memory stands, in proportion to the elapsed time.marklogic.hosts.save_write_rate (gauge)The moving average of writing data to in-memory stands. Shown as mebibytemarklogic.hosts.total_cpu_stat_system (gauge)Total cpu utilization for system. Shown as percentmarklogic.hosts.total_cpu_stat_user (gauge)Total cpu utilization for user. Shown as percentmarklogic.hosts.write_lock_rate (gauge)The rate of write lock acquistions. Shown as lockmarklogic.hosts.write_lock_wait_load (gauge)The total time spent holding write locks.marklogic.hosts.xdqp_client_receive_load (gauge)Time threads spent receiving data for the xdqp client, in proportion to the elapsed time.marklogic.hosts.xdqp_client_receive_rate (gauge)The moving average throughput of the XDQP client receiving data. Shown as mebibytemarklogic.hosts.xdqp_client_send_load (gauge)Time threads spent sending data for xdqp clients, in proportion to the elapsed time.marklogic.hosts.xdqp_client_send_rate (gauge)The moving average throughput of the xdqp clients sending data. Shown as mebibytemarklogic.hosts.xdqp_server_receive_load (gauge)Time threads spent receiving data for the xdqp server, in proportion to the elapsed time.marklogic.hosts.xdqp_server_receive_rate (gauge)The moving average throughput of the xdqp server receiving data. Shown as mebibytemarklogic.hosts.xdqp_server_send_load (gauge)Time threads spent sending data for the xdqp server, in proportion to the elapsed time.marklogic.hosts.xdqp_server_send_rate (gauge)The moving average throughput of the xdqp server sending data. Shown as mebibytemarklogic.requests.max_seconds (gauge)The maximum length in seconds for the active requests. Shown as secondmarklogic.requests.mean_seconds (gauge)The mean length in seconds for the active requests or the open transactions. Shown as secondmarklogic.requests.median_seconds (gauge)The median length in seconds for the active requests or the open transactions. Shown as secondmarklogic.requests.min_seconds (gauge)The minimum length in seconds for the active requests or the open transactions. Shown as secondmarklogic.requests.ninetieth_percentile_seconds (gauge)The length in seconds for the ninetieth percentile of the active requests. Shown as secondmarklogic.requests.query_count (gauge)The total number of active query requests. Shown as querymarklogic.requests.standard_dev_seconds (gauge)The standard deviation in seconds for the active requests or the open transactions. Shown as secondmarklogic.requests.total_requests (gauge)The total number of active requests. Shown as requestmarklogic.requests.update_count (gauge)The total number of active update requests. Shown as requestmarklogic.servers.expanded_tree_cache_hit_rate (gauge)The average number of hits on the expanded cache. Shown as hitmarklogic.servers.expanded_tree_cache_miss_rate (gauge)The average number of misses on the expanded cache. Shown as missmarklogic.servers.request_count (gauge)The rate of a request. Shown as requestmarklogic.servers.request_rate (gauge)The total number of requests for the cluster. Shown as requestmarklogic.transactions.max_seconds (gauge)The maximum length in seconds for the active transactions. Shown as secondmarklogic.transactions.mean_seconds (gauge)The mean length in seconds for the active requests or the open transactions. Shown as secondmarklogic.transactions.median_seconds (gauge)The median length in seconds for the active requests or the open transactions. Shown as secondmarklogic.transactions.min_seconds (gauge)The minimum length in seconds for the active requests or the open transactions. Shown as secondmarklogic.transactions.ninetieth_percentile_seconds (gauge)The length in seconds for the ninetieth percentile of the active requests. Shown as secondmarklogic.transactions.standard_dev_seconds (gauge)The standard deviation in seconds for the active requests or the open transactions. Shown as second
correct_foundationPlace_00033	FactBench	2	73	https://blog.knoldus.com/how-marklogic-server-is-used-in-different-industries/	en	How MarkLogic Server is used in different industries	https://blog.knoldus.com…76-108506-1.webp	https://blog.knoldus.com…76-108506-1.webp	[ "https://www.knoldus.com/wp-content/uploads/Knoldus-logo-1.png", "https://blog.knoldus.com/wp-content/uploads/2023/02/nastech-logo.svg", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/india.png", "https://www.knoldus.com/wp-content/uploads/2021/12/united-states.png", "https://www.knoldus.com/wp-content/uploads/2021/12/canada.png", "https://www.knoldus.com/wp-content/uploads/2021/12/singapore.png", "https://www.knoldus.com/wp-content/uploads/2021/12/netherlands.png", "https://www.knoldus.com/wp-content/uploads/2021/12/european-union.png", "https://blog.knoldus.com/wp-content/uploads/2022/07/search_icon.png", "https://www.knoldus.com/wp-content/uploads/Knoldus-logo-1.png", "https://blog.knoldus.com/wp-content/uploads/2023/02/nastech-logo.svg", "https://www.knoldus.com/wp-content/uploads/bars.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://blog.knoldus.com/wp-content/uploads/2022/07/plus.svg", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2017/06/knoldus_blocklogo.png?fit=220%2C53&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/06/cloud-storage-banner-background_53876-108506-1.webp?fit=740%2C493&ssl=1", "https://secure.gravatar.com/avatar/3f1f4dc837a878d185f723515378d244?s=110&d=monsterid&r=g", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/Knoldus-logo-final.png?fit=1447%2C468&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/nashtech-logo-white.png?fit=276%2C276&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/IOSTQB-Platinum-Partner-white.png?fit=268%2C96&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/cmmi5-white.png?fit=152%2C84&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-27001-white.png?fit=120%2C113&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-27002-white.png?fit=120%2C114&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2023/02/ISO-9001-white.png?fit=120%2C114&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-lightbend-white.png?fit=151%2C32&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-databricks-white-.png?fit=133%2C20&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-confluent-white.png?fit=147%2C28&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-docker-white.png?fit=112%2C29&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-hashiCorp-white.png?fit=144%2C31&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-ibm-white.png?fit=63%2C25&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-daml-white.png?fit=107%2C29&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-datastax-white.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-kmine-white.png?fit=138%2C36&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-rust-foundation-white.png?fit=138%2C43&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-scala-white-1.png?fit=107%2C46&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/knoldus-snowflake-white-1.png?fit=164%2C48&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/umbraco-1.png?fit=178%2C50&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/aws-partner-logo-1.png?fit=92%2C56&ssl=1", "https://i0.wp.com/blog.knoldus.com/wp-content/uploads/2022/04/Microsoft-Gold-Partner_white-1.png?fit=172%2C50&ssl=1" ]	[]	[]	[ "" ]	null	[ "Prakhar Rastogi" ]	2022-09-27T12:07:43+00:00	MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents in the data model for semantics.	en	https://blog.knoldus.com…2/04/favicon.png	Knoldus Blogs	https://blog.knoldus.com/how-marklogic-server-is-used-in-different-industries/	I will be walking through some of the cases studies and industry use cases that are being talked about in the official MarkLogic Solutions – https://www.marklogic.com/solutions/ MarkLogic Server currently operates in a variety of industries. Although the data retracted and extracted from MarkLogic differs in each sector, many customers have similar data management issues. Common issues include: Low cost Accurate and efficient search Enterprise-grade features Ability to store heterogeneous data from multiple sources in a single repository and make it immediately available for search Rapid application development and deployment Publishing/Media Industry BIG Publishing accepts data sources from publishers, wholesalers, and distributors and sells them information in data sources, web services, and websites, as well as through other proprietary solutions. Demand for the vast amount of information stored in the company’s database was high the company’s search solutions working with a conventional relational database were not effectively fulfilling that requirement. The company recognized that a new search solution was needed for customers to get relevant content from its huge database. The database had to handle 600,000 to 1 million updates per day while searching and when loading new content. The company was usually six to eight days behind schedule when a particular document would arrive at a time when it would be available to its customers. MarkLogic combines full-text search with the W3C standard XQuery language. MarkLogic the platform can simultaneously load, query, manipulate, and render content. When loading the content to MarkLogic, it is automatically converted to XML and indexed so it is instantly available for search. Hiring MarkLogic allowed the company to improve search capabilities through a combination of XML element query, XML proximity searching, and full-text search. MarkLogic’s XQuery interface searches the content and structure of XML data and facilitates access to XML content. It only took about 4 to 5 months company to develop solutions and implement them. Government / Public Sector XYZ Government wants to make it easier for county employees, developers, and residents to access real-time information about zoning changes, county ordinances, and property history. The county has volumes of data in different systems and in different formats the need to ensure more efficient access to data while maintaining the integrity of the recorded data. They need a solution that fits into their local IT infrastructure, can be implemented quickly and keeps hardware and license costs low and predictable. The solution is to migrate all existing PDF, Word, or CAD files from the county’s legacy systems to MarkLogic, which provides secure storage for all record data, easy-to-use search, and the ability to geospatially view results on a map. By centralizing their data in MarkLogic, district officials can access all the data they need from one central repository. MarkLogic allows the county to transform and enrich the data, as well as view and correlate it in a variety of ways using a variety of applications. Additionally, XYZ Government can make this information even more accessible to its constituents by deploying a publicly accessible web portal with powerful search capabilities on top of the same central MarkLogic repository. Financial Services Industry ABC Services Inc. provides financial research to customers on a subscription basis. Because every second counts in the fast-paced world of stock trading, the company needs to deliver new research to its subscribers as quickly as possible to help them make better decisions about their trades. Unfortunately, this effort was hampered by the company’s outdated infrastructure. Due to the shortcomings of the current tool, they were unable to easily respond to new requirements or fully utilize the documents being created. In addition, they could not meet their targets for timely delivery of alerts. ABC Services has replaced its legacy system with MarkLogic Server. Now the company can take full advantage of the information from the research. The solution significantly reduces alert latency and delivers information to the customer’s portal and email. In addition, the ability to create triple indexes and perform semantic searches greatly improved the user experience. With the new system, ABC Services provides timely research to 80,000 users worldwide, improving customer satisfaction and competitive advantage. By alerting customers more quickly to the availability of critical new research, financial traders gain a definitive edge in the office and on the trading floor. Other Industries Other industries benefiting from MarkLogic Server include: Government Intelligence — Identify patterns and discover connections from massive amounts of heterogeneous data. Airlines — Flight manuals, service records, customer profiles. Insurance — Claims data, actuary data, regulatory data. Education — Student records, test assembly, online instructional material. Legal — Laws, regional codes, public records, case files. References: http://www.marklogic.com/solutions/
correct_foundationPlace_00033	FactBench	1	51	https://www.kmworld.com/Articles/News/News/Unveiling-MarkLogic-Server-4.2-70991.aspx	en	Unveiling MarkLogic Server 4.2	https://dzceab466r34n.cl…r-Images-ORG.png		[ "https://dzceab466r34n.cloudfront.net/KMWorld/TemplateImages/KM-Logo.svg", "https://dzceab466r34n.cloudfront.net/Images/OtherImages/165017-2024-Cover-Images-ORG.png", "https://dzceab466r34n.cloudfront.net/images_nl/sw/32x32_Circle_49_FB.png", "https://dzceab466r34n.cloudfront.net/Images/OtherImages/160917-X-Logo-ORG.png", "https://dzceab466r34n.cloudfront.net/images_nl/sw/32x32_Circle_49_LI.png", "https://dzceab466r34n.cloudfront.net/images_nl/sw/32x32_Circle_49_YT.png", "https://dzceab466r34n.cloudfront.net/Images/IssueImage/165181-724WP.jpg-ORG.jpg", "https://dzceab466r34n.cloudfront.net/Images/IssueImage/165065-PRocedureFlowspecRep.jpg-ORG.jpg", "https://dzceab466r34n.cloudfront.net/Images/IssueImage/164319-LucidworksCKlist.jpg-ORG.jpg", "https://dzceab466r34n.cloudfront.net/images_nl/KMWorld/KMW20_100-Com_Banner_300x100.jpg", "https://dzceab466r34n.cloudfront.net/Images/OtherImages/126049-2019-Trendsetting-Products-ORG.png", "https://dzceab466r34n.cloudfront.net/KMWorld/TemplateImages/KM-Logo.svg", "https://dzceab466r34n.cloudfront.net/images_nl/sw/32x32_Circle_49_LI.png", "https://dzceab466r34n.cloudfront.net/Images/OtherImages/160917-X-Logo-ORG.png", "https://dzceab466r34n.cloudfront.net/images_nl/sw/32x32_Circle_49_FB.png" ]	[]	[]	[ "Content Management", "Enterprise Search", "Knowledge Management", "Education", "Energy", "Financial Services", "Government", "Healthcare", "Legal", "Manufacturing", "Pharmaceutical", "Life Sciences", "Media/Entertainment", "Telecom", "Transportation", "Aerospace" ]	null	[]	2010-10-25T00:00:00-04:00	Includes ETL tool for unstructured information	en		KMWorld	https://www.kmworld.com/Articles/ReadArticle.aspx?ArticleID=70991	MarkLogic has launched Version 4.2, which features the MarkLogic Information Studio—claimed to be the industry’s first extract, transform and load (ETL) tool for managing unstructured information— as well as new features for availability and recovery (including full database replication). MarkLogic 4.2 is available for download here. MarkLogic calls Version 4.2 a new type of database that allows organization to fully exploit unstructured information such as documents, social media posts, e-mails, tweets, images, videos, blogs and research data. With Information Studio, customers can simply drag and drop files or point to a file directory to load into MarkLogic. When combined with other application services including Application Builder, Information Studio is said to deliver significant benefits in terms of accelerating development of new information applications while reducing the cost of operations of MarkLogic. New features in MarkLogic 4.2 include:
correct_foundationPlace_00033	FactBench	2	53	https://www.wikiwand.com/en/MarkLogic_Server	en	MarkLogic Server	https://wikiwandv2-19431…s/icon-32x32.png	https://wikiwandv2-19431…s/icon-32x32.png	[ "https://wikiwandv2-19431.kxcdn.com/_next/image?url=https://upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Marklogic-logo.PNG/640px-Marklogic-logo.PNG&w=640&q=50", "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/Ambox_rewrite.svg/40px-Ambox_rewrite.svg.png", "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Marklogic-logo.PNG/250px-Marklogic-logo.PNG", "https://upload.wikimedia.org/wikipedia/en/thumb/8/8a/OOjs_UI_icon_edit-ltr-progressive.svg/10px-OOjs_UI_icon_edit-ltr-progressive.svg.png" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data.	en	https://wikiwandv2-19431…icon-180x180.png	Wikiwand	https://www.wikiwand.com/en/MarkLogic_Server	MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data.[1]
correct_foundationPlace_00033	FactBench	1	5	https://docs.marklogic.com/guide/ref-arch/intro	en	Understanding the Reference Architecture (Reference Application Architecture Guide) — MarkLogic Server 11.0 Product Documentation			[ "https://docs.marklogic.com/images/ML-Logo-1.png", "https://docs.marklogic.com/images/i_pdf.png", "https://docs.marklogic.com/apidoc/images/printerFriendly.png", "https://docs.marklogic.com/media/apidoc/11.0/guide/ref-arch/intro/intro-1.gif", "https://docs.marklogic.com/media/apidoc/11.0/guide/ref-arch/intro/intro-2.gif", "https://docs.marklogic.com/media/apidoc/11.0/guide/ref-arch/intro/intro-3.gif" ]	[]	[]	[ "marklogic", "enterprise nosql database", "enterprise nosql", "database", "nosql", "nosql database", "nosql db", "xml", "xml database", "json", "enterprise", "bigdata", "big data", "xquery", "xslt", "petabyte", "java db", "java database", "content", "content store", "content database", "content db", "content management system", "CMS", "document", "document-oriented databases", "document database", "document db", "document store", "DB", "xml database", "xml db", "json db", "nonrelational", "nonrelational database", "nonrelational db" ]	null	[]	null	MarkLogic is the only Enterprise NoSQL Database	en			null	Understanding the Reference Architecture The MarkLogic Reference Application Architecture is a three-tier application template and set of best practices for architects, developers, and administrators designing, developing, and deploying applications that use MarkLogic Server. This guide covers the following topics:
correct_foundationPlace_00033	FactBench	1	47	https://forge.puppetlabs.com/modules/myoung34/marklogic/readme	en	marklogic · Puppet MarkLogic management module · Puppet Forge	https://forge.puppetlabs.com/favicon.ico	https://forge.puppetlabs.com/favicon.ico	[ "https://forge.puppetlabs.com/_next/static/media/forgeLogo.011ede18.svg 1x, /_next/static/media/forgeLogo.011ede18.svg 2x", "https://travis-ci.org/myoung34/puppet-marklogic.png?branch=master,dev", "https://coveralls.io/repos/myoung34/puppet-marklogic/badge.png", "https://d2weczhvl823v0.cloudfront.net/myoung34/puppet-marklogic/trend.png", "https://forge.puppetlabs.com/_next/static/media/puppet.17d5e08a.svg 1x, /_next/static/media/puppet.17d5e08a.svg 2x" ]	[]	[]	[ "" ]	null	[ "Marcus Young" ]	null	Puppet MarkLogic management module	en	/favicon.ico	Puppet Forge	https://forge.puppet.com/modules/myoung34/marklogic/readme	GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. {one line to give the program's name and a brief idea of what it does.} Copyright (C) {year} {name of author} This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: {project} Copyright (C) {year} {fullname} This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
correct_foundationPlace_00033	FactBench	2	69	https://docs.marklogic.com/guide/concepts/use-cases	en	How is MarkLogic Server Used? (Concepts Guide) — MarkLogic Server 11.0 Product Documentation			[ "https://docs.marklogic.com/images/ML-Logo-1.png", "https://docs.marklogic.com/images/i_pdf.png", "https://docs.marklogic.com/apidoc/images/printerFriendly.png" ]	[]	[]	[ "marklogic", "enterprise nosql database", "enterprise nosql", "database", "nosql", "nosql database", "nosql db", "xml", "xml database", "json", "enterprise", "bigdata", "big data", "xquery", "xslt", "petabyte", "java db", "java database", "content", "content store", "content database", "content db", "content management system", "CMS", "document", "document-oriented databases", "document database", "document db", "document store", "DB", "xml database", "xml db", "json db", "nonrelational", "nonrelational database", "nonrelational db" ]	null	[]	null	MarkLogic is the only Enterprise NoSQL Database	en			null	Publishing/Media Industry BIG Publishing receives data feeds from publishers, wholesalers, and distributors and sells its information in data feeds, web services, and websites, as well as through other custom solutions. Demand for the vast amount of information housed in the company's database was high and the company's search solution working with a conventional relational database were not effectively meeting that demand. The company recognized that a new search solution was necessary to help customers retrieve relevant content from its enormous database. The database had to handle 600,000 to 1 million updates a day while it is being searched and while new content is being loaded. The company was typically six to eight days behind from when a particular document would come in to when it would be available to its customers. MarkLogic combines full-text search with the W3C-standard XQuery language. The MarkLogic platform can concurrently load, query, manipulate and render content. When content is loaded into MarkLogic, it is automatically converted into XML and indexed, so it is immediately available for search. Employing MarkLogic enabled the company to improve its search capabilities through a combination of XML element query, XML proximity search, and full-text search. MarkLogic's XQuery interface searches the content and the structure of the XML data, making that XML content more easily accessible. It took only about 4 to 5 months for the company to develop the solution and implement it. The company discovered that the way in which MarkLogic stores data makes it easier for them to make changes in document structure and add new content when desired. With the old relational database and search tools, it was very difficult to add different types of content. Doing so, used to require them to rebuild the whole database and that would take 3 to 4 weeks. With MarkLogic, they can now restructure documents and drop in new document types very quickly. Another key benefit is the cost savings the company has realized as a result of the initiative. The company needed a full-time employee on staff to manage their old infrastructure. Now, the company has an employee who spends one-quarter of his time managing the MarkLogic infrastructure. The company saves on the infrastructure side internally and their customers get the content more quickly. Government / Public Sector The Quakezone County government wants to make it easier for county employees, developers and residents to access real-time information about zoning changes, county land ordinances, and property history. The county has volumes of data in disparate systems and in different formats and need to provide more efficient access to the data, while maintaining the integrity of the record data. They need a solution that fits within county IT infrastructure, that can be quickly implemented, and that keeps the hardware and licensing costs both low and predictable. The solution is to migrate all of existing PDF, Word, or CAD files from the county's legacy systems into MarkLogic, which provides a secure repository for all of the record data, easy-to-use search and the ability to display the results in a geospatial manner on a map. By having their data centralized in MarkLogic, county clerks can access all of the data they need from one central repository. MarkLogic enables the county to transform and enrich the data, as well as to view and correlate it in multiple ways by multiple applications. Tasks that once took days or weeks to accomplish can now be completed in seconds or minutes. Additionally, Quakezone County can make this information even more accessible to its constituents by deploying a public-facing web portal with powerful search capabilities on top of the same central MarkLogic repository. Financial Services Industry TimeTrader Services Inc. provides financial research to customers on a subscription basis. Because every second counts in the fast-paced world of stock trading, the firm needs to deliver new research to its subscribers as quickly as possible to help them make better decisions about their trades. Unfortunately, these efforts were hampered by the firm's legacy infrastructure. Because of shortcomings with the current tool they were not able to easily respond to new requirements or to fully leverage the documents that were being created. Additionally they could not meet their goals for delivering alerts in a timely fashion. TimeTrader Services replaced its legacy system with a MarkLogic Server. Now the firm can take full advantage of the research information. The solution drastically reduces alert latency and delivers information to the customer's portal and email. In addition, the ability to create triple indexes and do semantic searches has vastly improved the user experience. Thanks to the new system, TimeTrader Services delivers timely research to 80,000 users worldwide, improving customer satisfaction and competitive advantage. By alerting customers to the availability of critical new research more quickly, financial traders gain a definite edge in the office and on the trading floor. Healthcare Industry HealthSmart is a Health Information Exchange (HIE) that is looking into using new technologies as a differentiating factor for success. They seek a technology advantage to solve issues around managing and gaining maximum use of a large volume of complex, varied, and constantly changing data. The number of requests for patient data and the sheer volume of that data are growing exponentially, in communities large and small, whether serving an integrated delivery network (IDN), hospital, or a large physician practice. These challenges include aggregating diverse information types, finding specific information in a large dataset, complying with changing formats and standards, adding new sources, and maintaining high performance and security, all while keeping costs under control. HIE solutions that solve big data challenges must meet the strict requirements of hospitals, IDNs and communities to lead to an effective and successful exchange. To develop a successful HIE, communities need to embrace technologies that help with key requirements around several important characteristics: Performance: The system should be able to provide real time results. As a result, doctors can get critical test results without delay. Scalability: As data volumes grow, the system should be able to scale quickly on commodity hardware with no loss in performance. Hospitals can then easily accommodate data growth in systems critical to patient care. Services: An effective exchange should have the option of rich services such as search, reporting, and analytics. Doctors will be notified if a new flu trend has developed in the past week in a certain geographic location. Systems: both of which will impact the quality, risks and costs of care. Interoperability: It should be easy to integrate different systems from other members of the community into the exchange through a common application programming interface (API). Community members can leverage the exchange sooner to share data and improve patient care. Security: Only authenticated and authorized users will be allowed to view private data. Community members want to ensure patient privacy and also comply with regulations such as HIPAA. Time to delivery: Implementation should be measured in weeks, not months or years and overhead should remain low. Healthcare can save millions in maintenance and hardware with low overhead, next generation technology. Total cost of ownership: The system must make economic sense. It should help to cut healthcare costs, not increase them. By leveraging MarkLogic, HealthSmart gained a significant performance boost that reduced queries and transformations to sub-second response times, which was critical for accomplishing their mission. In addition, MarkLogic's flexible data model enabled integration of new sources of data in a matter of days instead of weeks or months. MarkLogic can efficiently manage billions of documents in the hundreds of terabytes range. It offers high speed indexes and optimizations on modern hardware to deliver sub-second responses to users. HealthSmart leverages the performance and scalability benefits of MarkLogic not only to quickly deliver information to users, but also to grow the deployment as the load grows. Consequently, HealthSmart provides the wide range of functionality required in information heavy healthcare environments today. Features such as full-text search, granular access to information, dynamic transformation capabilities, and a web services framework all lower the development overhead typically required with other technologies. This makes HealthSmart a feature-rich HIE solution that does not make the tradeoffs that other solutions make. HealthSmart is particularly advantageous with regard to interoperability. Since MarkLogic is based on XML, and XML is widely used in healthcare systems, the technology fit is ideal. The ability to load information as is dramatically lowers the barrier of adding new systems in an HIE community. This, in part, enabled HealthSmart to build the patient registry component in a mere two months, far faster than any other vendor. HealthSmart's dynamic transformation capabilities facilitate compliance with transport standards and regulatory legacy interfaces. And MarkLogic's services-oriented architecture (SOA) and its support for building REST endpoints enable an easy and standardized way to access information. As an enterprise-class database, MarkLogic supports the security controls needed to keep sensitive information private. MarkLogic is used in top secret installations in the Federal Government, and provides access controls to ensure classified data is only accessible to authorized personnel. Finally, HealthSmart and MarkLogic help to significantly lower time-to-delivery and the total cost of ownership. The lower overhead in adding new community members directly leads to quick adoption and cost savings. MarkLogic's optimization on modern, commodity hardware enables exchanges to benefit from lower cost hardware systems. High performance enables systems with fewer hardware servers, and scalability allows growth by simply adding more commodity servers, rather than replacing existing servers with larger, high cost servers.
correct_foundationPlace_00033	FactBench	1	84	https://stackoverflow.com/questions/45241124/using-xdmpplan-in-marklogic	en	Using xdmp:plan in MarkLogic	https://cdn.sstatic.net/…g?v=73d79a89bded	https://cdn.sstatic.net/…g?v=73d79a89bded	[ "https://i.sstatic.net/J6pYX.png?s=64", "https://www.gravatar.com/avatar/6cf3c25436a6b28cec78ede71f140df0?s=64&d=identicon&r=PG&f=y&so-version=2", "https://www.gravatar.com/avatar/a35a1b505714a75a51178d42c16a8db8?s=64&d=identicon&r=PG&f=y&so-version=2", "https://stackoverflow.com/posts/45241124/ivc/e7cd?prg=19ebce13-02c7-44a5-9d3d-093080ed0839" ]	[]	[]	[ "" ]	null	[]	2017-07-21T15:12:40	I wanted to compare two queries: 1) xdmp:plan(fn:distinct-values(/ts:top-song/ts:genres/ts:genre/text(), "http://marklogic.com/collation/en/S1/AS/T00BB")) 2) declare variable $options := <op...	en	https://cdn.sstatic.net/Sites/stackoverflow/Img/favicon.ico?v=ec617d715196	Stack Overflow	https://stackoverflow.com/questions/45241124/using-xdmpplan-in-marklogic	xdmp:plan does not take an arbitrary expression as its operand: it looks like a function but it really is not. (If you think about it, that must be the case, because if it were a function it would evaluate its arguments first, so it would have no basis for creating the plan.) It is not designed to give you a comparison of two general XQuery expressions, but of the index operations involved in a search or path. You can only give it either an XPath or a cts:search expression. So: xdmp:plan(ts:top-song/ts:genres/ts:genre/text())
correct_foundationPlace_00033	FactBench	2	28	https://www.opentext.com/	en	Information Management Solutions	https://www.opentext.com…age_Facebook.png	https://www.opentext.com…age_Facebook.png	[ "https://www.opentext.com/assets/images/shared/opentext-image-lp-information-reimagined-en.png", "https://www.opentext.com/assets/images/shared/opentext-image-trusted-information-en.png", "https://www.opentext.com/assets/images/shared/bunny.png", "https://www.opentext.com/assets/images/shared/opentext-image-ai-and-security-built-in-en.png", "https://www.opentext.com/assets/images/shared/opentext-image-knowledge-reimagined-en.png", "https://www.opentext.com/assets/images/shared/opentext-image-cloudops-reimagined-en-v2.png", "https://www.opentext.com/assets/images/shared/opentext-image-connections-reimagined-en.png", "https://www.opentext.com/assets/images/shared/opentext-image-conversations-reimagined-en.png", 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"https://www.opentext.com/assets/images/HowCanWeHelp-Contact-Us.svg" ]	[]	[]	[ "" ]	null	[]	null	OpenText offers cloud-native solutions in an integrated and flexible Information Management platform to enable intelligent, connected and secure organizations.	en	/assets/images/favicon.png	OpenText	https://www.opentext.com	Business Clouds Advance your enterprise data management, data governance, and data orchestration to be AI ready. Learn more Business AI Let the machines do the work and apply AI with automation to advance your business. Learn more
correct_foundationPlace_00033	FactBench	1	92	https://stackshare.io/stackups/couchdb-vs-marklogic	en	What are the differences?	https://img.stackshare.i…e2ea62eaca83.jpg	https://img.stackshare.i…e2ea62eaca83.jpg	[ "https://img.stackshare.io/fe/SOC2.png" ]	[]	[]	[ "" ]	null	[]	null	CouchDB - HTTP + JSON document database with Map Reduce views and peer-based replication. MarkLogic - Schema-agnostic Enterprise NoSQL database technology, coupled w/ powerful search & flexible application services.	en		StackShare	https://stackshare.io/stackups/couchdb-vs-marklogic
correct_foundationPlace_00033	FactBench	2	90	https://www.htcinc.com/	en	Let's Make Digital Change Happen	https://www.htcinc.com/w…eams-image-6.png	https://www.htcinc.com/w…eams-image-6.png	[ "https://www.htcinc.com/wp-content/uploads/2021/12/joinus-logo.png", "https://www.htcinc.com/wp-content/uploads/2024/06/GPTW-Announce-Website-homepage-Banner-JUN-2024-V4.jpg", "https://www.htcinc.com/wp-content/uploads/2024/03/ISG-Retail_Analytics-Services-Quadrant-Report-Dec-23_Home-page-banner_with-badge.jpg", "https://www.htcinc.com/wp-content/uploads/2022/04/Patterns_red.png", "https://www.htcinc.com/wp-content/uploads/2024/03/RMN-Banner-Image_2000x830.jpg", "https://www.htcinc.com/wp-content/uploads/2023/10/Homepage-Banner.png", "https://www.htcinc.com/wp-content/uploads/2023/08/ISG-Retail_Home-page-banner_revised.jpg", "https://www.htcinc.com/wp-content/uploads/2021/12/Vector.png", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/downArrow.png", 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"https://www.htcinc.com/wp-content/uploads/2021/12/eSpeak-1.png", "https://www.htcinc.com/wp-content/uploads/2021/12/downArrow.png", "https://www.htcinc.com/wp-content/uploads/2022/01/JoinHTC-new.jpg", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/facebook.png", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/twitter.png", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/linkedin.png", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/youtube.png", "https://www.htcinc.com/wp-content/themes/htc/assets/images/img/instagram-white.png", "https://wpfc.ml/b.gif" ]	[]	[]	[ "HTC global services", "htc global services website", "htc global", "htc inc", "htcinc", "htcinc.com", "htc global service", "global services", "htc global services inc", "it services", "it solutions", "it global solution", "global services", "global solutions", "service global" ]	null	[]	2021-12-17T09:16:27+00:00	HTC Global Services provides IT and Business Process Services and Solutions that help businesses make digital change happen. Here’s how.	en	https://www.htcinc.com/w…eams-image-6.png	htcinc	https://www.htcinc.com/	We are quite impressed with the innovative solution offered by HTC on this project that has substantially improved the workflow. Your experience in managing projects of this magnitude was evident from your anticipation of issues and addressing those issues in a productive manner. A Large Retailer I express the appreciation of the University for the excellent work from HTC during the implementation of Kuali Financial System. The work of HTC was crucial in bringing the University Kuali Financial System live on very tight timeframe. A Leading University I have found HTC Team to be courteous, professional and attentive to our needs throughout the exploratory, implementation and ongoing management process of our project together. They have been prompt with their responses to any questions. A Leading Health Insurance Company We were impressed with the speed of development process. There were no times during this phase where we felt the offshore piece of the project was at risk of falling behind schedule. Your team actually completed the work ahead of schedule which meant our users could spend more time testing which always helps. An Automotive Giant
correct_foundationPlace_00033	FactBench	2	74	https://adamfowler.org/2013/07/14/data-modelling-in-marklogic-and-how-my-mate-norm-can-help-you/	en	Data Modelling in MarkLogic, and how my mate Norm can help you!…	https://s0.wp.com/i/blank.jpg	https://s0.wp.com/i/blank.jpg	[ "https://1.gravatar.com/avatar/79f885323aee1f16e54b24e9fb5b9624b7701587ec8b9727ad38290043e7c48f?s=56&d=identicon&r=G", "https://0.gravatar.com/avatar/3538f02c43002d137bda89e567393250c8576a471f0463ffd937982e2a3fa11d?s=56&d=identicon&r=G", "https://s2.wp.com/i/logo/wpcom-gray-white.png", "https://s2.wp.com/i/logo/wpcom-gray-white.png", "https://pixel.wp.com/b.gif?v=noscript" ]	[]	[]	[ "" ]	null	[]	2013-07-14T00:00:00	Data modelling in an aggregate/document NoSQL database can be unfamiliar to most. In this post I mention a couple of techniques and how they can help... In both relational database systems and NoSQL key-value and columnar stores you have to take a logical application 'object' and shred it in to one or more structures that…	en	https://s1.wp.com/i/favicon.ico	Adam's Deep Technology Blog	https://adamfowler.org/2013/07/14/data-modelling-in-marklogic-and-how-my-mate-norm-can-help-you/	Data modelling in an aggregate/document NoSQL database can be unfamiliar to most. In this post I mention a couple of techniques and how they can help… In both relational database systems and NoSQL key-value and columnar stores you have to take a logical application ‘object’ and shred it in to one or more structures that are flat. They are typically table based, with some NoSQL options supported one or more values per column, or some sort of built in hash mapping function. Shredding poses a few problems for application developers. These mainly centre around them having to do work just to store and retrieve their information. As a lazy bunch (I used to be one, so I can get away with that comment!) they don’t like spending time doing this – they prefer to spend time on the interesting and unique stuff. This is normally an issue because organisations want these storage layers to be fast and reliable – so they force their developers (quite rightly) to spend time on this layer too. What if you didn’t need a layer like this though? Why not just store an aggregate pretty much as is – no matter how complex the internal structure? This is where XML and JSON come in. As hierarchical data formats they can be used to easily map to and from application code. There are many techniques for just taking an object and converting to and from XML and JSON. There’s little, if any, mapping code to write, and these layers are typically open source and so have been tuned to within an inch of their life already. This is a win-win for both developers and organisations. Developers can spend time on the ‘interesting’ stuff. Project times are cut. More profit (or less cost) is made for the organisation. Developers are happy, and organisations are happy. Also, the ‘interesting’ areas are typically your organisations specific business, or application’s key differentiators – they are your secret sauce and so it makes sense to spend more time working on that than your competition. This is why aggregate / document databases like Mongo DB and MarkLogic are popular with developers. MarkLogic projects typically last only 3-6 months, including detailed specification and installation. The BBC’s Olympics architecture build MarkLogic system was like this, and look how great that worked during the Olympics. The longest I’ve heard of in a Phase 1 development with MarkLogic is 12 months. Load as-is Mapping from application code to XML or JSON is one thing. What about all the other stuff you need to store and retrieve later? MarkLogic can handle storing binary files efficiently, including replication very large (multi GB) files across a cluster if needs be. It’s logical to store all content for an application in one repository rather than several – an RDBMS for transaction info, a ECM system for office docs, a MongoDB for web app docs – it all adds to the complexity, and of course cost in maintenance and development, of a final application. We talk a lot about loading ‘as-is’ in MarkLogic. Sometimes this is misinterpreted. I wanted to spend a little time talking about the implications here. Let’s say you’ve got a MarkLogic system and are loading in docs from SharePoint and application information in XML. They all relate to your customers and so it makes sense to store them in a system where you can say ‘Show me everything we have about customer X’. You can happily store these in MarkLogic. We even have a SharePoint connector to make the process simpler. Store an XML representation of everything There is a bit of a problem though. MarkLogic search indexes plain text and XML files (and JSON that is stored as XML transparently to application code). In order to make the text and properties searchable we use over 200 ISYS filters built in to MarkLogic to create an XHTML rendering of the document. This is purely for indexing purposes. You can maintain a link in this document back to the original – just add an XML element called originaldoc or some such in the XHTML head section. The advantage of doing this may not be immediately apparent. It looks like on the face of it you’re just using more storage for the same documents. In fact, this is highly desirable in all but the most basic applications. Most organisations, especially in the Public Sector where I work, do not allow the original document to be altered. In order to make a document searchable though you may need to know what content within it represent a customer code, or date, order number, place, post code, organisation – the list goes on. The best way to do this is to tag those words. This is easy to do in XML because you can wrap the text with an XML element in another name space – like <ns1:placename>Scunthorpe</ns1:placename>. This gives you a consistent information tagging mechanism across all your documents – regardless of source type – that can be used for searching. Of course, doing this on the original would mean altering your binary docs, let alone having to figure out how to do this tagging differently for every type of document you store. Much better, therefore, to create an XHTML (an XML based format of HTML) rendering and then perform this entity extraction function on them. You also then have the option of enriching this information. I could, for example, add a longitude and latitude attribute to my placename element. This would mean rather than search for a list of places I can instead search for a point and radius, or bounding box, or even draw a polygon on a map and say ‘what do we know about here?’. This approach gives you great flexibility in enriching information as the basis of your application. It also means you can improve the enrichment or identify new entities without a rewrite of the system – you leave the original docs alone and just re-run the new extraction script against your old data. Voila! A very rich search application or data repository. What about relational data? MarkLogic tends to be used to aggregate related data from many source systems as an initial deployment, prior to being used as the primary data source itself. After all you have to have a working system before you can switch application code to use that system. This tends to mean we get asked about storing ‘relational data’ a lot. This could be some CSV/TSV exports, or even a direct dump of data from an RDBMS. MarkLogic deals with aggregates – documents – and thus doesn’t need the equivalent of an SQL join. The problem with these data dumps though is that they tend to be one file for an entire table’s data, or one file per row, and are very flat in structure. Consider a relational schema with Order, OrderItem, Item, Customer and Address tables. For a single order you may have over 10 rows of data spread across tables. Although you may want to store the originals, you more than likely also want to store a composite document that encompasses all information for an ‘Order’. You may even want to create an Order History document that holds all items and quantities a person has ever ordered. These are fundamentally tree structures that make sense to model in a single XML document. Denormalisations The question is how do you go about creating these composites? You can of course re-write an app to use MarkLogic directly. Long term this is the least costly approach. Everyone has legacy data though, so lets discuss how to remodel that assuming you now have a set of 10 or so flat documents containing information for a single order. You effectively need a task that looks in this data and joins it together. In MarkLogic parlance this is called a Denormalisation. You are going from a normalised relational model to a denormalised document model. This may mean you store the same information multiple times – like Item details within Item, composite Order, and composite Customer Order History documents. The advantages though are faster query time. This is in effect the Document database version of a relational View. You create a view when a query with horrendous joins is killing query time. No real difference here. So how to go about creating these things? You could use a batch task – but what if the originating system is in flight and you’re getting dumps periodically? Worse still, what if an Order Item document is committed (as in ACID commit) to MarkLogic before the logical parent Order document? In this scenario you need to use a trigger or Content Processing Framework (CPF) pipeline to initiate the job. You may create one per target denormalisation doc you create. At the start of this you’ll load related information to the new doc that has been added, and do a sanity check to ensure all required information for your denormalisation is present. You’ll then test the hell out of it to make it performant. You will of course want to be careful. You wouldn’t want to create a trigger storm accidentally where the creation of a denormalisation document causes another trigger to fire, then another – the MarkLogic equivalent of a fork bomb. (Remember these from your Computer Science University days when a friend would remotely login to your machine and execute his little special programme as you’re working on an essay? Yes Matt H I’m looking at you!) Partial Normalisation As well as denormalising shredded documents in to a single composite there may be scenarios where you want to go the other way. Consider our order composite document:- <order id=”order1″> <deliveryaddress>1 Paddington Way, London</deliveryaddress> <orderitem> <code>item1</code> <quantity>50</quantity> </orderitem> <orderitem> <code>item2</code> <quantity>4</quantity> </orderitem> </order> MarkLogic has an ODBC server where you can set up relational views over unstructured data. This is a great way to use your existing BI tool to query not only a relational data warehouse, but also your live, operational unstructured MarkLogic database. This works by each ‘column’ referring to a range index set up on a particular element, attribute or path in XML stored in MarkLogic. A ‘row’ in the view represents a document (more accurately, fragment – I’ll leave that for another day) that has a value in the range index for every required ‘column’. Some of course can be nillable, and thus optional. This is great, but does occassionally lead to unexpected results. In the above document, for example, range indexes over order id, item code, and quantity would logically – to a human – result in these two rows in the view:- order1 : item1 : 50 order1 : item2 : 4 Instead they correctly (by the mathematics involved) resolve to these four rows:- order1 : item1 : 50 order1 : item1 : 4 order1 : item2 : 50 order1 : item2 : 4 If you think about it this makes sense. There are two values for both the item code and quantity range indexes. The ODBC view does not infer any containment from the parent <orderitem> element because it is simply a co-occurence over range index values. You can get around this to some extent by adding a fragment boundary on orderitem. This tells MarkLogic about logical containment. This has implications though on storage and search. I won’t bore you with the details, but you have to be careful. What you need is a halfway house between a fully normalised set of 10 or so relational flat documents, and the one order document. You need a document per logical row in your ODBC view. You need these two documents, in effect:- <order id=”order1″> <deliveryaddress>1 Paddington Way, London</deliveryaddress> <orderitem> <code>item1</code> <quantity>50</quantity> </orderitem> <madeby>Generated by Adams awesome shredding script</madeby> </order> and <order id=”order1″> <deliveryaddress>1 Paddington Way, London</deliveryaddress> <orderitem> <code>item2</code> <quantity>4</quantity> </orderitem> <madeby>Generated by Adams awesome shredding script</madeby> </order> (The <madeby> is optional – I was just testing static element functionality! More on this below…) This can be done by semi-normalising the composite denormalised document. (Confused by the terminology yet? Hang in there, help is on the way!) It’s a similar pattern to before, except you partially shred the composite Order document in to Full Order Item documents. Full On Shredding Occasionally you’ll be storing a dump of data as a document and need to shred it. This is common in conversions from CSV and TSV files to XML, or even in a single Excel sheet if you think about it. You need to take a single document and shred it to hell. Not quite a ‘Normalisation’ pattern because you don’t care if there’s an Address1, Address2, Address3 or repeated data – you just need to handle each row as a single document. Same mechanism though really as the partial normalisation, but simpler mathematically and bigger performance wise (assuming 10 000 rows in a data dump rather than 10 for a single transaction). Where to start developing? You’re probably thinking this sounds a lot of work. You’d be right. There are many, many benefits though. Also because you’re working with tree structures as opposed to lots of columns across tables there’s less work than writing mapping code as Data Access Objects (DAOs). For example, a delivery address element’s entire content can be referenced using the XPath /order/deliveryaddress/* rather than as individual ‘column’ names. This means you can add and remove data to delivery address and not have to alter your scripts. It is a pain though. Lots of very similar code isn’t the most exciting thing to write. I’d start by thinking of what you want to have as ‘search results’ or ‘application composites’. A composite Order is a good example. Customer is another good one. Once this is done, check out your source data and create a denormalisation as required to feed this new composite. After this, if the composite is not quite suited to an ODBC view (and indeed you need that functionality) then write a partial normalisation against that new denormalised version. This way you don’t have to start from shredded data – you can take advantage of the fact all the data you need is present in the denormalised document already. It’s still creating triggers or CPF pipelines by coding XQuery rather than configuring a tool to help you out though. What if you have lots of these, or don’t have the testing time in your project? Meet my mate Norm Help is at hand! I hate giving people problems, I prefer giving them solutions. So this week whilst zipping up and down England on the train I’ve started a project to do this work for you, based on configuration in an XML file per denormalisation rather than writing triggers yourself. I’ve called it Norm (because he works both ways – denormalisation and partial normalisation (shredding) ). Let me introduce him to you. It’s pretty straightforward to list the source documents, whether they are required or not, and how they relate to each other, and detail what your target structure looks like. It’s harder to write the code to do it yourself. This is what I’ve provided in Norm. I’ve used my knowledge of MarkLogic indexes and functions to provide a tested, performant (if you remember to remove the xdmp:log statements in production) library and samples to get your started quickly. Here’s a denormalisation configuration for creating my Order composite:- xquery version “1.0-ml”; declare namespace n = ‘http://marklogic.com/norm/main’; xdmp:document-insert(“/admin/config/norm-test.xml”,<n:denormalisation> <n:name>ODBC Shred</n:name> <n:description>Shred document for ODBC view</n:description> <n:uri-pattern>/prog-avail/##s1:uri##-##auto##-odbc.xml</n:uri-pattern> <n:collections>norm-generated,odbc-data</n:collections> <n:enabled>true</n:enabled> <n:template> <n:element name=”order”> <n:attribute name=”id” source=”s1″ source-path=”/order/@id/fn:data()” /> <n:element name=”deliveryaddress” source=”s1″ source-path=”/order/deliveryaddress/text()” /> <n:element name=”orderitem” source=”s1″ source-path=”/order/orderitem/*”/> <n:static> <madeby>Generated by Adams awesome shredding script</madeby> </n:static> </n:element> </n:template> <n:sources> <n:source id=”s1″ name=”order” root-xpath=”fn:collection(“”odbc””)” mode=”create-update” required=”true” shred=”/order/orderitem”> <n:collection-match>odbc</n:collection-match> <n:primary-key><n:element>order</n:element><n:attribute>id</n:attribute></n:primary-key> </n:source> </n:sources> </n:denormalisation>, xdmp:default-permissions(),”norm-config” ) As you can see it’s pretty straightforward – you list what the target doc (or docs) look like, where the data comes from, then use XPath to ask Norm (we like to ask, not order Norm – he’s a sensitive fellow) to place data in particular locations. I’ve also supported common replacement patterns in the URI to make this easy too. I’ve even allowed static text or elements to be included. This particular script executes against the above data in 0.013 seconds. Most of this is around logging code. Without logging code it executes in 0.0064 seconds – double the speed. Indexes are required to get this speed though. I have a helper function to show you for a particular denormalisation which indexes you will need. How do I enlist Norm? Install instructions are out of the scope of this document. I’ll probably record a video soon to cover it off. For now though you can head on over to my Norm GitHub page to grab the code and read the instructions and design methodology. I’ve only handled a few basic structures so far. If you have a complex set of relationships (E.g. grandparent rather than just parent), or other specific use cases, please log an issue on GitHub with samples if possible, even just basic mockups, and I’ll add that functionality in. Naturally I’ll be working on full documentation and test scripts before our community manager, Eric Bloch, tells me off! 8o) (Hi Eric! … have you met Norm yet? Norm, this is Eric…) Summary Hopefully you’ve seen a couple of useful ideas for a future unstructured data management application you’re looking to build. If there are any questions I can always be reached at adam dot fowler at marklogic dot com. Say Goodbye Norm!!!
correct_foundationPlace_00033	FactBench	1	21	https://db-engines.com/en/system/MariaDB%253BMarkLogic	en	MariaDB vs. MarkLogic Comparison			[ "https://db-engines.com/matomo/matomo.php?idsite=2&rec=1", "https://db-engines.com/db-engines.png", "https://db-engines.com/pictures/extremedb/extremedb-problem-iot-connectivity.jpg", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/ranking_trend_s.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/info.png", "https://db-engines.com/pictures/Email.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/Facebook.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/LinkedIn.svg", "https://db-engines.com/pictures/X.svg", "https://db-engines.com/pictures/Mastodon.svg", "https://db-engines.com/pictures/Bluesky.png", "https://db-engines.com/pictures/milvus.svg", "https://db-engines.com/pictures/singlestore_250x80.png", "https://db-engines.com/pictures/Neo4j-logo_color_sm.png", "https://db-engines.com/pictures/datastax-fp.png", "https://db-engines.com/pictures/raimadb.png" ]	[]	[]	[ "" ]	null	[]	null	Detailed side-by-side view of MariaDB and MarkLogic	en			null	What to be aware before importing MariaDB .sql into a MySQL database? 11 July 2024, SitePoint MariaDB plc: Shareholders speak, but execs are quiet 22 May 2024, InfoWorld Private equity offer for MariaDB gets thumbsup from shareholders 21 May 2024, The Register RECOMMENDED CASH OFFER for MARIADB PLC by MERIDIAN BIDCO LLC which is an Affiliate of K1 INVESTMENT MANAGEMENT, LLC as manager of K5 PRIVATE INVESTORS, L.P. 17 June 2024, PR Newswire ServiceNow trades MariaDB for RaptorDB (PostgreSQL) 13 May 2024, Techzine Europe provided by Google News Intelligence for multi-domain warfighters can now be sourced from logistics operations 13 May 2024, Breaking Defense Seven Quick Steps to Setting Up MarkLogic Server in Kubernetes 1 February 2024, release.nl Progress's $355m move for MarkLogic sets the tone for 2023 4 January 2023, The Stack Progress to acquire PE-backed data platform MarkLogic for $355m 4 January 2023, PE Hub Progress Completes Acquisition of MarkLogic 7 February 2023, GlobeNewswire provided by Google News
correct_foundationPlace_00033	FactBench	1	60	https://techcrunch.com/2009/05/26/mark-logic-raises-125-million-for-xml-server-software/	en	Mark Logic Raises $12.5 Million For XML Server Software	https://techcrunch.com/w…ax-250x250-2.png	https://techcrunch.com/w…ax-250x250-2.png	[ "https://techcrunch.com/wp-content/themes/tc-23/dist/svg/tc-logo.svg", "https://techcrunch.com/wp-content/uploads/2009/05/20964v1-max-250x250-2.png", "https://techcrunch.com/wp-content/uploads/2024/07/24.07.18-Kakao-founder-Brian-Kim-.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/Wiz-Founders.-Credit-Avishag-Shaar-Yashuv-e1720979215109.webp?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/monarch-Dairy-Riley.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/eric-2.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/RidhimaHeadshot.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-1662708140-e1721664527112.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/volunteer-disrupt-2024-header.png?w=1024", "https://techcrunch.com/wp-content/uploads/2022/01/GettyImages-1314979456.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2018/06/swiggy.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Fragment_TechCrunch.png?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/GettyImages-1268601565.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/pesa.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Asus-ROG-Ally-X-3.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/women-in-ai-raman.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2020/08/GettyImages-1090431902.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/President-Biden-Speaks-On-The-Economy.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/05/Sundar-AI-backdrop-Google-IO.png?w=947", "https://techcrunch.com/wp-content/uploads/2024/07/wazirx-app.jpeg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Arkady-Volozh-10-e1721378622488.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/fallout-4.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/crowdstrike-windows-airplane-glitch-v1.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-2161842016.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/GettyImages-2162660378.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/Brad-Barket-Stringer.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/06/Hale_MikeMartin_79-1-e1720555795355.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/rnc-2024-report-v3.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2023/08/ev-battery-factories-2.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/faulty-crowdstrike-update.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2024/07/cisa-crowdstrike-cybersecurity-outage.jpg?w=1024" ]	[]	[]	[ "" ]	null	[ "Leena Rao" ]	2009-05-26T00:00:00	Mark Logic, an IT company that creates software to host large amounts of content, has raised $12.5 million in Series D funding led by Sequoia Capital,	en	https://techcrunch.com/w…radient.png?w=32	TechCrunch	https://techcrunch.com/2009/05/26/mark-logic-raises-125-million-for-xml-server-software/	Mark Logic, an IT company that creates software to host large amounts of content, has raised $12.5 million in Series D funding led by Sequoia Capital, with participation from Tenaya Capital. This latest round brings Mark Logic’s total funding to $45.5 million. Founded in 2001, Mark Logic says it will use the funding to grow sales channels, expand to international markets and develop new verticals. Mark Logic’s software is an XML server that allows users to store content in a platform and serves as a platform for rich applications.
correct_foundationPlace_00033	FactBench	1	37	https://www.globaldata.com/company-profile/marklogic-corp/premium-data/installbase/	en	MarkLogic Corp Install Base	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico	[ "https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 400w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 800w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 1600w", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-number-fade.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-target.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/newsletterIntro.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/bg-report-singular-dark.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/site/peak.webp", "https://assets.globaldata.com/gdcom/assets/img/bg/marketing/PremiumDatabases/installbase.webp", "https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 400w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 800w, https://assets.globaldata.com/gdcom/assets/img/logo/gd_blue-3-1-1.webp 1600w" ]	[]	[]	[ "" ]	null	[]	null	MarkLogic Corp - Install Base provides its users the ability to understand a prospect’s technology landscape and a vendor’s geography or sector level product deployment	en	https://assets.globaldata.com/gdic/assets/img/icon/favicon.ico		https://www.globaldata.com/company-profile/marklogic-corp/premium-data/installbase/	Have you found what you were looking for? From start-ups to market leaders, uncover what they do and how they do it.
correct_foundationPlace_00033	FactBench	2	19	https://www.knowi.com/marklogic	en	MarkLogic Reporting And Visualization	https://www.knowi.com/im…avicon-32x32.png	https://www.knowi.com/im…avicon-32x32.png	[ "https://www.knowi.com/images/section3-img.png", "https://www.knowi.com/images/section2-img.png", "https://www.knowi.com/images/section1-img.png", "https://www.knowi.com/images/home/partner1.svg", "https://www.knowi.com/images/home/partner2.svg", "https://www.knowi.com/images/home/partner3.svg", "https://www.knowi.com/images/home/partner4.svg", "https://www.knowi.com/images/home/partner5.svg", "https://www.knowi.com/images/home/partner6.svg", "https://www.knowi.com/images/home/partner7.svg", "https://www.knowi.com/images/home/partner8.svg", "https://www.knowi.com/images/home/partner9.svg", "https://www.knowi.com/images/homenew/logo.svg", "https://www.knowi.com/images/home/twitter.svg", "https://www.knowi.com/images/home/linkedin.svg", "https://www.knowi.com/img/alertWidgetLimit.webp", "https://www.knowi.com/images/home/arrow-white.svg", "https://www.knowi.com/images/home/arrow-black.svg", "https://www.knowi.com/images/home/arrow-black.svg", "https://www.knowi.com/images/home/arrow-black.svg", "https://www.knowi.com/images/home/arrow-black.svg", "https://www.knowi.com/images/home/arrow-black.svg", "https://www.knowi.com/images/navbar/database.svg", "https://www.knowi.com/images/navbar/file-code-o.svg", "https://www.knowi.com/images/navbar/file-code-o.svg", "https://www.knowi.com/images/navbar/video-camera.svg", "https://www.knowi.com/images/navbar/shield.svg", "https://www.knowi.com/images/navbar/users.svg" ]	[]	[]	[ "MarkLogic Visualization", "MarkLogic Reporting", "MarkLogic BI", "MarkLogic Business Intelligence" ]	null	[]	null	Knowi offers native BI integration into MarkLogic		/images/icons/favicons/apple-touch-icon.png		https://www.knowi.com/marklogic	Services. 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To obtain access to certain Services, you will be required to obtain an Account with Knowi by completing a registration form and designating a user ID and password. Until you apply for and are approved for an Account, your access to the Services will be limited to those areas of the Services, if any, that Knowi makes available to the general public. You agree and represent that all registration information you provide is accurate, complete, and current, and that you will update it promptly when that information changes. Knowi may withdraw Account approval at any time in its sole discretion, with or without cause. You are responsible for safeguarding the confidentiality of your User ID and passwords, and for all activities that take place with your Account. Knowi will not be liable for any loss or damage arising from any unauthorized use of your Account. Notices from Knowi. You acknowledge that once you have registered with us, we may send you communications or data regarding the Services using electronic means. These may include, but are not limited to (i) notices about your use of the Services, including any notices concerning violations of use, (ii) updates to the Services, (iii) promotional information and materials regarding Knowi's products and services, and information the law requires us to provide. We give you the opportunity to opt-out of receiving certain of these communications from us by following the opt-out instructions provided in the message. However, even if you opt-out, you understand that we may continue to provide you with required information by e-mail at the address you specified when you signed up for the Services or via access to a website that we identify. Notices we e-mail to you will be deemed given and received when the e-mail is sent. If you don't agree to receive required notices via e-mail, you must stop using the Services. If you provide Knowi with legal notices, you must transmit it to us via email to legal@cloud9charts.com. Any such notice, in either case, must specifically reference that it is a notice given under these Terms. Notices from You regarding Unauthorized Use. You agree to notify us promptly in writing when you become aware of any unauthorized use of an Account, the Content or the Services, including if you suspect there has been any loss, theft or other security breach of your password or user ID. If there is an unauthorized use by a third party which obtained access to the Services through you or your Users, whether directly or indirectly, you agree to take all steps necessary to terminate the unauthorized use. You also agree to provide Knowi with any cooperation and assistance related to that unauthorized use which we reasonably request. Content. Knowi does not monitor any data transmitted or processed hrough, or stored in, the Services. You agree that you: are responsible for the accuracy and quality of all Content that is transmitted or processed through, or stored in, your Account; will ensure that the Content (including its storage and transmission) complies with these Terms, and applicable laws and regulations; will promptly handle and resolve any notices and claims from a third party claiming that any Content violates that party's rights, including regarding take-down notices pursuant to the Digital Millennium Copyright Act; will maintain appropriate security, protection and backup copies of the Content, which may include (A) the use of encryption technology to protect the Content from unauthorized access and (B) routine archiving of the Content. Knowi will have no liability of any kind as a result of any deletion, loss, correction, or destruction of Content or damage to or failure to store or encrypt any Content. Use Restrictions. You are responsible for Users' compliance with these Terms and for the quality, accuracy and legality of the Content. You will not, and will ensure that your Users do not use the Services in any manner or for any purpose other than as expressly permitted by these Terms including, without limitation, allowing Power Users to use the logins of your Business Partner Users; sell, rent, resell, lease, or sublicense the Services to any third party; modify, tamper with or otherwise create derivative works of the Services; reverse engineer, disassemble or decompile the Services, or attempt to derive source code from the Services; remove, obscure or alter any proprietary right notice related to the Services; use the Services to send unsolicited or unauthorized junk mail, spam, chain letters, pyramid schemes or any other form of duplicative or unsolicited messages; store or transmit Content: (A) containing unlawful, defamatory, threatening, pornographic, abusive, or libelous material, (B) containing any material that encourages conduct that could constitute a criminal offense, or (C) that violates the intellectual property rights or rights to the publicity or privacy of others; use the Services to store or transmit viruses, worms, time bombs, Trojan horses or other harmful or malicious code, files, scripts, agents or programs; interfere with or disrupt servers or networks connected to the Services or the access by other Knowi client to the servers or networks, or violate the regulations, policies or procedures of those networks; access or attempt to access Knowi's other accounts, computer systems or networks not covered by these Terms, through password mining or any other means; or access or use the Services in a way intended to avoid incurring fees, exceeding usage limits and the like. Third Party Services and Content. All transactions using the Services are between the transacting parties only. The Services may contain features and functionalities linking or providing you with certain functionality and access to third party content, including Web sites, directories, servers, networks, systems, information and databases, applications, software, programs, products or services, and the Internet as a whole. You acknowledge that Knowi is not responsible for such content or services. We may also provide some content to you as part of the Services. However, Knowi is neither an agent of any transacting party nor a direct party in any such transaction. Any of those activities, and any terms associated with those activities, are solely between you and the applicable third-party. Similarly, we are not responsible for any third party content you access with the Services, and you irrevocably waive any claim against Knowi with respect to such sites and third-party content. Knowi has no liability, obligation or responsibility for any such correspondence, purchase or promotion between Customer and any such third-party. You are solely responsible for making whatever investigation you feel is necessary or appropriate before proceeding with any transaction with any of these third parties and your dealings with any third party related to the Services, whether online or offline, including the delivery of and payment for goods and services. In the event you have any problems resulting from your use of a third party service, or suffer data loss or other losses as a result of problems with any of your other service providers or any third-party services, we are not responsible unless the problem was the direct result of our breaches. Fees. You agree to pay, using a valid credit card (or other form of payment which we may accept from time to time), the charges and fees (such as recurring monthly or annual fees) set forth in Schedule A, Taxes (as defined below), and other charges and fees incurred in order to access the Services. You will pay Fees in the currency we quoted for your account (and we reserve the right to change the quoted currency at any time). We will automatically charge your credit card or other account at the start of the billing period and at the start of each renewal period. Except as specifically set forth in this section, all Services are prepaid for the period selected (monthly, annually or otherwise) and are non-refundable. This includes accounts that are renewed. Fees for Upgrade. If you upgrade or expand consumption of the Services , additional fees may be due at Knowi's then-current pricing. If additional fees are due, those fees will be immediately charged to your credit card or other account and will apply for the entire month in which the Services Upgrade occurred. If you have paid for an annual period, Services Upgrades will be coterminous with the affected Services period. Fee Increases. We will notify you in advance, either through a posting on this Website or by email to the address you have most recently provided to us, if we increase Fees or institute new charges or fees. Any increase in Fees will take effect at the beginning of the next renewal subscription term for the Services. For example, if you pay monthly, your use of the Services will be charged at the new price when Services are renewed in the month that follows the notice. If you don't agree to these changes, you must cancel and stop using the Services. Invoicing and Payment Terms. You agree to keep all information in your billing account current. You may change your payment method or modify your billing account information at any time by using the means provided on the Website. Your notice to us will not affect charges we submit to your billing account before we reasonably could act on your request. In the event that we invoice you, then all fees will be due and payable upon receipt. We reserve the right to charge, and you agree to pay, a late fee on past due amounts. The late fee will be equal to the lesser of 1.5% of the unpaid amount each month or the maximum amount allowed by applicable law. We may use a third party to collect past due amounts. You must pay for all reasonable costs we incur to collect any past due amounts, including reasonable attorneys' fees and other legal fees and costs. In addition, we may suspend your access to the Services, or cancel the Services, if your account is past due. Taxes. Fees are exclusive of Taxes and you will pay or reimburse Knowi for all Taxes arising out of these Terms, whether assessed at the time of your purchase or are thereafter determined to have been due. For purposes of these Terms, "Taxes" means any sales, use and other taxes (other than taxes on Knowi's income), export and import fees, customs duties and similar charges applicable to the transactions contemplated by these Terms that are imposed by any government or other authority. You agree to promptly provide Knowi with legally sufficient tax exemption certificates for each taxing jurisdiction for which you claim exemption. Description of Confidential Information. In connection with each party's rights and obligations under these Terms, each party (as the "disclosing party") may disclose to the other party (as the "recipient") certain of its confidential or proprietary information ("Confidential Information"). In the case of Knowi, the Services, these Terms and any other proprietary or confidential information we provide to you constitute Knowi Confidential Information. In the case of Customer, Content provided to Knowi by Customer constitutes Customer Confidential Information. Protection of Confidential Information. Each party as recipient agrees: (i) to exercise at least the same degree of care to safeguard Confidential Information of the disclosing party as the recipient exercises to safeguard the confidentiality of its own confidential information, but not less than reasonable care; (ii) to use the disclosing party's Confidential Information only in connection with exercising its rights and performing its obligations under these Terms; and (iii) to not disclose or disseminate the disclosing party's Confidential Information to any third party and that the only employees and contractors who will have access to the disclosing party's Confidential Information will be those with a need to know who have agreed to abide by the obligations set forth in this Section pursuant to a written confidentiality agreement. Protection of Content. We agree to maintain appropriate administrative, physical, and technical safeguards to protect the security, confidentiality, and integrity of the Content. The third party data center providers utilized by Knowi in the provision of the Services will maintain at a minimum SSAE 16 audit certification or its equivalent. Except as requested by you in connection with customer support, we will not (i) modify Content, (ii) disclose Content except pursuant to the requirements of a governmental agency, by operation of law, to investigate occurrences that may involve violations of system or network security, or as you expressly permit in writing, or (iii) access Content except to provide the Services or to address other service or technical problems. >Exceptions to Confidentiality. Information will not be deemed Confidential Information of either of us under these Terms if such information: (i) is or becomes rightfully known to the recipient without any obligation of confidentiality or breach of these Terms; (ii) becomes publicly known or otherwise ceases to be secret or confidential, except through a breach of these Terms by the recipient of such Confidential Information; or (iii) is independently developed by the recipient of such Confidential Information without breach of these Terms. Confidential Information will remain the property of the disclosing party. General. Knowi reserves the right to temporarily suspend or terminate your access to the Services at any time in Knowi's sole discretion, with or without cause, and with or without notice, without incurring liability of any kind. For example, we may suspend or terminate your access to or use of the Services for: (i) the actual or suspected violation of these Terms; (ii) the use of the Services in a manner that may cause Knowi to have legal liability or disrupt others' use of the Services; (iii) the suspicion or detection of any malicious code, virus or other harmful code in your Account; (iv) downtime, whether scheduled or recurring; (e) your use of excessive storage capacity or bandwidth; or (v) unplanned technical problems and outages. If, in our determination, the suspension might be indefinite or we have elected to terminate your access to the Services, we will use commercially reasonable efforts to notify you through the Services. You acknowledge that if your access to the Services is suspended or terminated, you may no longer have access to the Content that is stored with the Services. Termination for Lack of Activity. In addition to our other rights of termination, if your Account is not currently subject to a paid subscription plan with us, we may terminate your Account if: (i) you do not engage in any activity in the Account within 30 days after registering for the Services, or (ii) you do not engage in any activity in an Account for 120 consecutive days. In the event of such termination, any of your Content may be lost. Post-Termination Obligations. Upon termination of these Terms for any reason, all of your rights to use or access the Services will cease. You agree, within five days of such termination, to destroy all copies of the Software, the Documentation, and any Confidential Information of Knowi, including any Documentation in written or electronic form and any Software stored on your servers or other systems. In addition, if requested by Knowi, you will promptly provide to Knowi a written certification signed by an authorized representative certifying that all copies of the Software and any written or electronic documentation and Confidential Information of Knowi have been destroyed. For 30 days following the expiration of the Termination of these Terms or the applicable subscription term for which you have paid, and subject to your prior written request, we will grant you with limited access to the Services solely for purposes of your retrieval of the Content. After that 30-day period, Knowi has no further obligation to maintain the Content and will delete the Content unless legally prohibited. Survival. The terms of any sections that by their nature are intended to extend beyond termination will survive termination of these Terms for any reason. Governing Law. These Terms will be construed and enforced in all respects in accordance with the laws of the State of California, without reference to its choice of law rules. Any dispute between the parties will be brought in a court in Alameda County and each party irrevocably waives any claim that such court does not have personal jurisdiction over the party. All use of the Services is expressly governed by any applicable export and import laws, and Customer must comply with all such laws. Claims arising out or related to these terms must be filed within one year of the date on which the claim arose unless local law requires a longer time to file claims. If a claim is not filed accordingly, then it is permanently barred. Government Users. If you are a U.S. government entity, you acknowledge that any Software and Documentation are provided as "Commercial Items" as defined at 48 C.F.R. 2.101, and are being licensed to U.S. government end users as commercial computer software subject to the restricted rights described in 48 C.F.R. 2.101 and 12.212. Independent Contractors; Third Party Beneficiaries. You and we are independent contractors, and nothing in these Terms creates a partnership, employment relationship or agency. There are no third-party beneficiaries of these Terms. Knowi may subcontract portions of the Services provided that Knowi shall remain responsible for all such obligations under these Terms. Waiver. Our failure to enforce any of these Terms will not be considered a waiver of the right to enforce them. Our rights under these Terms will survive any termination. Assignment. You may not assign these Terms or your rights and obligations under them, in whole or in part, to any third party without our prior written consent, and any attempt by you to do so will be invalid. Severability. Should any part of these Terms be held invalid or unenforceable, that portion will be construed consistent with applicable law and the remaining portions will remain in full force and effect. Force Majeure. Neither party will be liable to the other for any delay or failure to perform its obligations under these Terms (excluding payment obligations) if the delay or failure arises from any cause or causes beyond that party's reasonable control. Public Announcement. Knowi reserves the right to release a press announcement regarding the parties' relationship, and to include Customer's name on Knowi's customer lists on Knowi's web site and in any other marketing materials. Entire Agreement and Changes. These Terms, including fees for Services on the Website, constitutes the entire agreement, and supersedes any and all prior agreements, between the parties with regard to the subject matter hereof. Knowi reserves the right to modify or replace these Terms at any time in its sole discretion. Knowi will indicate at the top of these Terms the date these Terms were last updated. Any changes will be effective upon posting the revised version of these Terms on the Services (or such later effective date as may be indicated at the top of the revised Terms). Customer's continued access or use of any portion of the Services constitutes Customer's acceptance of such changes. If Customer doesn't agree to any of the changes, Customer must cancel and stop using the Services. Privacy. In order to operate and provide the Services, Knowi collect certain information about Customer. As part of the Services, Knowi may also automatically upload information about Customer's computer or other device, Customer's use of the Services, and the Services performance. Knowi will use and protect that information as described in the privacy policy located on the Website ("Privacy Policy"). Customer further acknowledges and agrees that Knowi may access or disclose information about Customer, including the content of Customer communications, in order to: (i) comply with the law or respond to lawful requests or legal process; (ii) protect the rights or property of Knowi or Knowi's customers, including the enforcement of Knowi's agreements or policies governing Customer's use of the Services; or (iii) act on a good faith belief that such access or disclosure is necessary to protect the personal safety of Knowi employees, customers, or the public. DMCA. We respect the intellectual property of others, and reserve the right to delete or disable Content that appears to violate these terms or applicable law. The Digital Millennium Copyright Act of 1998 (the "DMCA") provides recourse for copyright owners who believe that material appearing on the Internet infringes their rights under U.S. copyright law. If you believe in good faith that Content infringes your copyright, you (or your agent) may send us a notice requesting that the Content be removed or access to it blocked. Federal law requires that your notification include the following information: (i) a physical or electronic signature of a person authorized to act on behalf of the owner of an exclusive right that is allegedly infringed; (ii) identification of the copyrighted work claimed to have been infringed or, if multiple copyrighted works at a single online site are covered by a single notification, a representative list of such works at that site; (iii) identification of the material that is claimed to be infringing or to be the subject of infringing activity and that is to be removed or access to which is to be disabled and information reasonably sufficient to permit us to locate the material; (iv) information reasonably sufficient to permit us to contact you, such as an address, telephone number, and, if available, an electronic mail; (v) a statement that you have a good faith belief that use of the material in the manner complained of is not authorized by the copyright owner, its agent, or the law; and (vi) a statement that the information in the notification is accurate, and under penalty of perjury, that you are authorized to act on behalf of the owner of an exclusive right that is allegedly infringed.
correct_foundationPlace_00033	FactBench	2	5	https://stackoverflow.com/questions/75286646/where-i-can-find-marklogic-configuration-file-in-windows	en	Where I can find Marklogic configuration file in Windows	https://cdn.sstatic.net/…g?v=73d79a89bded	https://cdn.sstatic.net/…g?v=73d79a89bded	[ "https://cdn.sstatic.net/Img/teams/overflowai.svg?v=d706fa76cdae", "https://i.sstatic.net/Te6t0.jpg?s=64", "https://lh4.googleusercontent.com/-UjnkX8KKR9U/AAAAAAAAAAI/AAAAAAAAAAA/AAKWJJN9NB9J0_ZIbMUiWLMORuYKrqNaCA/photo.jpg?sz=64", "https://www.gravatar.com/avatar/e478f2600848a3dc5238b7ffb2e148f6?s=64&d=identicon&r=PG", "https://www.gravatar.com/avatar/9268b4da576af1de16541ab29f2e4c0d?s=64&d=identicon&r=PG&f=y&so-version=2", "https://stackoverflow.com/posts/75286646/ivc/d13a?prg=b5a201ad-ffb8-4cd2-98e4-717061518aa6" ]	[]	[]	[ "" ]	null	[]	2023-01-30T15:35:31	I need to edit Marklogic config file in my local Windows environment. I want to edit the time zone option in the marklogic.conf file in order for MarkLogic to operate with a different time zone set...	en	https://cdn.sstatic.net/Sites/stackoverflow/Img/favicon.ico?v=ec617d715196	Stack Overflow	https://stackoverflow.com/questions/75286646/where-i-can-find-marklogic-configuration-file-in-windows	It really depends on what you're trying to archive. On Windows, you will find MarkLogic typically being installed under C:\Program Files\MarkLogic. The installation process has also created a Windows service pointing to the marklogic.exe executable for you to run as a background service. The characteristics of the service can be altered using the Services snap-in on the Microsoft Management Console. You can start it by simply typing Services on the start menu. Let me know if you're trying to archive anything in particular.
correct_foundationPlace_00033	FactBench	2	97	https://enlyft.com/tech/products/marklogic	en	Companies using MarkLogic and its marketshare	https://enlyft.com/tech/…ts/marklogic.png	https://enlyft.com/tech/…ts/marklogic.png	[ "https://enlyft.com/tech/static/images/enlyft-logo-120.svg 120w, /tech/static/images/enlyft-logo-139.svg 139w", "https://enlyft.com/tech/static/images/static_charts/marklogic.png", "https://enlyft.com/tech/static/images/static_charts/marklogic.png", "https://enlyft.com/tech/static/images/twitter.svg", "https://enlyft.com/tech/static/images/facebook.svg", "https://enlyft.com/tech/static/images/linkedin.svg", "https://enlyft.com/tech/static/images/static_charts/marklogic_industry.png", "https://enlyft.com/tech/static/images/static_charts/marklogic_country.png", "https://enlyft.com/tech/static/images/static_charts/marklogic_employee_range.png", "https://enlyft.com/tech/static/images/static_charts/marklogic_revenue_range.png", "https://enlyft.com/tech/static/images/close.svg", "https://enlyft.com/tech/static/images/customers-logos/Microsoft.png", "https://enlyft.com/tech/static/images/customers-logos/Intuit.png", "https://enlyft.com/tech/static/images/customers-logos/Pax8.png", "https://enlyft.com/tech/static/images/customers-logos/PwC.png", "https://enlyft.com/tech/static/images/customers-logos/Capgemini.png", "https://enlyft.com/tech/static/images/customers-logos/Dell.png", "https://enlyft.com/tech/static/images/customers-logos/Unum.png", "https://enlyft.com/tech/static/images/customers-logos/Chargebee.png", "https://enlyft.com/tech/static/images/enlyft-logo.svg", "https://enlyft.com/tech/static/images/linkedin-24.png", "https://enlyft.com/tech/static/images/facebook-24.png", "https://enlyft.com/tech/static/images/twitter-24.png" ]	[]	[]	[ "" ]	null	[]	null	1,530 companies use MarkLogic. MarkLogic is most often used by companies with 50-200 employees & $>1000M in revenue. Our usage data goes back 7 years and 11 months.	en	/tech/static/images/icon-logo.png		https://enlyft.com/tech/products/marklogic	What happens once I submit a request? Someone from the Enlyft team will get back to you within 24 hours with more information. How much is the cost? The cost depends on various factors, such as number of records, number of products and use of advanced filtering and search criteria. Will I start getting spam on my email? Definitely not! We will not be adding you to an email list or sending you any marketing materials without your permission.
correct_foundationPlace_00033	FactBench	2	78	https://www.zippia.com/marklogic-careers-30392/history/	en	MarkLogic History: Founding, Timeline, and Milestones	https://static.zippia.co…pia-og-image.png	https://static.zippia.co…pia-og-image.png	[ "https://static.zippia.com/ui-router/images/timeline-arrow-down.svg", "https://static.zippia.com/ui-router/images/timeline-arrow-down.svg", "https://static.zippia.com/ui-router/images/timeline-arrow-down.svg", "https://static.zippia.com/ui-router/images/timeline-arrow-down.svg", "https://static.zippia.com/ui-router/images/timeline-arrow-down.svg", "https://static.zippia.com/images/cmp-history/founded.svg 1x, https://static.zippia.com/images/cmp-history/founded.svg 2x", "https://static.zippia.com/images/cmp-history/headquarters.svg 1x, https://static.zippia.com/images/cmp-history/headquarters.svg 2x", "https://static.zippia.com/images/career-demographics/average-employee-age.svg 1x, https://static.zippia.com/images/career-demographics/average-employee-age.svg 2x", "https://www.zippia.com/ui-router/images/company-history/get-updates.svg 1x, /ui-router/images/company-history/get-updates.svg 2x", "https://static.zippia.com/ui-router/images/waving-zebra.svg 1x, https://static.zippia.com/ui-router/images/waving-zebra.svg 2x", "https://static.zippia.com/ui-router/logo/medium.png", "https://static.zippia.com/ui-router/images/footer/zippi-homepage.svg 1x, https://static.zippia.com/ui-router/images/footer/zippi-homepage.svg 2x" ]	[]	[]	[ "" ]	null	[]	2020-08-27T00:00:00-08:00	A complete timeline of MarkLogic's History from founding to present including key milestones and major events.	en	/ui-router/images/favicon.ico		https://www.zippia.com/marklogic-careers-30392/history/	Zippia gives an in-depth look into the details of MarkLogic, including salaries, political affiliations, employee data, and more, in order to inform job seekers about MarkLogic. The employee data is based on information from people who have self-reported their past or current employments at MarkLogic. The data on this page is also based on data sources collected from public and open data sources on the Internet and other locations, as well as proprietary data we licensed from other companies. Sources of data may include, but are not limited to, the BLS, company filings, estimates based on those filings, H1B filings, and other public and private datasets. While we have made attempts to ensure that the information displayed are correct, Zippia is not responsible for any errors or omissions or for the results obtained from the use of this information. None of the information on this page has been provided or approved by MarkLogic. The data presented on this page does not represent the view of MarkLogic and its employees or that of Zippia. MarkLogic may also be known as or be related to MarkLogic, MarkLogic Corp, MarkLogic Corp., MarkLogic Corporation and Marklogic Corporation.
correct_foundationPlace_00033	FactBench	1	83	https://www.theknowledgeacademy.com/blog/marklogic-vs-mongodb/	en	MarkLogic Vs MongoDB: Which one is better?	https://www.theknowledge…ome/tka-blue.svg	https://www.theknowledge…ome/tka-blue.svg	[ "https://www.theknowledgeacademy.com/_public/images/home/tkalogo.svg", "https://www.theknowledgeacademy.com/_public/images/home/header-search.svg", "https://www.theknowledgeacademy.com/_public/images/home/right-icon.svg", "https://www.theknowledgeacademy.com/_public/images/home/head-close.svg", "https://www.theknowledgeacademy.com/_public/images/course/down-hover.svg", "https://www.theknowledgeacademy.com/_public/images/course/down-hover.svg", "https://www.theknowledgeacademy.com/_public/images/home/cta-arrow-white.svg", "https://www.theknowledgeacademy.com/_public/images/home/header-search.svg", "https://www.theknowledgeacademy.com/_public/images/course/breadcrum-arrow.svg", "https://www.theknowledgeacademy.com/_public/images/course/breadcrum-arrow.svg", "https://www.theknowledgeacademy.com/_public/images/course/breadcrum-arrow.svg", "https://www.theknowledgeacademy.com/_public/images/blog/book.svg", "https://www.theknowledgeacademy.com/_public/images/blog/stars.png", "https://www.theknowledgeacademy.com/_files/images/MarkLogic_Vs_MongoDB.png", "https://www.theknowledgeacademy.com/_files/images/MarkLogic_vs_MongoDB_Key_differences.png", "https://www.theknowledgeacademy.com/_files/images/MongoDB_developer%281%29.png", "https://www.theknowledgeacademy.com/_public/images/pla_images/building.svg", "https://www.theknowledgeacademy.com/_public/images/career/cross.svg", "https://www.theknowledgeacademy.com/_public/images/home/red-star.png", "https://www.theknowledgeacademy.com/_public/images/home/red-star.png", "https://www.theknowledgeacademy.com/_public/images/home/red-star.png", "https://www.theknowledgeacademy.com/_public/images/home/red-star.png", "https://www.theknowledgeacademy.com/_public/images/home/red-star.png", "https://www.theknowledgeacademy.com/_public/images/home/search-close.svg", "https://www.theknowledgeacademy.com/_public/images/home/header-search.svg", "https://www.theknowledgeacademy.com/_public/images/home/voice.svg", "https://www.theknowledgeacademy.com/_public/images/home/cta-arrow.svg", "https://www.theknowledgeacademy.com/_public/images/home/search-close.svg", "https://www.theknowledgeacademy.com/_public/images/home/alert.svg", "https://www.theknowledgeacademy.com/_public/images/home/search-close.svg", "https://www.theknowledgeacademy.com/_public/images/home/search-close.svg", "https://www.theknowledgeacademy.com/_public/images/home/search-close.svg", "https://www.theknowledgeacademy.com/_public/images/course/back-btn.svg", "https://www.theknowledgeacademy.com/_public/images/course/close.svg", "https://www.theknowledgeacademy.com/_public/images/home/close-new.svg", "https://www.theknowledgeacademy.com/_public/images/home/flash.svg", "https://www.theknowledgeacademy.com/_public/images/home/cta-arrow-black.svg", "https://www.theknowledgeacademy.com/_public/images/course/back-btn.svg", "https://www.theknowledgeacademy.com/_public/images/course/close2.svg", "https://www.theknowledgeacademy.com/_public/images/home/cta-arrow-black.svg", "https://www.theknowledgeacademy.com/_public/images/course/back-btn.svg", "https://www.theknowledgeacademy.com/_public/images/course/close.svg", "https://www.theknowledgeacademy.com/_public/images/course/back-btn.svg", "https://www.theknowledgeacademy.com/_public/images/course/close.svg", "https://www.theknowledgeacademy.com/_public/images/home/tkalogo.svg", "https://www.theknowledgeacademy.com/_public/images/home/medium.svg" ]	[]	[]	[ "marklogic vs mongodb", "marklogic mongodb" ]	null	[]	null	In this blog, we will conduct a comprehensive comparison of MarkLogic vs MongoDB, exploring their key differences and suitability for different scenarios.	en	https://www.theknowledgeacademy.com/favicon.ico		https://www.theknowledgeacademy.com/blog/marklogic-vs-mongodb/	Choosing the right Database solution is crucial for businesses to stay competitive in the modern domain of Data Management. Two popular NoSQL Databases, MarkLogic and MongoDB, offer a set of unique features and capabilities that cater to different use cases. In this blog, we will conduct a comprehensive comparison of MarkLogic vs MongoDB, exploring their key differences and suitability for different scenarios. Table of Contents 1) What are MarkLogic and MongoDB? 2) MarkLogic vs MongoDB: Key differences a) Data model and schema b) Query capabilities c) Scalability and performance d) Security e) Use cases 3) Conclusion What are MarkLogic and MongoDB? MarkLogic is a robust, enterprise-grade NoSQL Database that excels in handling complex data integration, semantics, and advanced search capabilities. It is known for its ACID (Atomicity, Consistency, Isolation, Durability) compliance, which makes it a reliable option for mission-critical applications and data-intensive industries like finance and healthcare. MongoDB, on the other hand, is a widely-used document-based NoSQL Database. Its schema-free design and horizontal scaling capabilities make it ideal for handling large volumes of unstructured data. MongoDB's flexibility and ease of use have made it a popular choice among developers, especially in web and mobile applications. Master MongoDB for app and web development – register for our MongoDB Developer Training now! MarkLogic vs MongoDB: Key differences This section of the blog will expand on the key differences between MarkLogic and MongoDB. Data model and schema MarkLogic and MongoDB have very different data models that accommodate different data formats. MarkLogic: MarkLogic's data model is designed to accommodate a wide variety of data formats, including XML, JSON, and RDF. This flexibility allows organisations to seamlessly integrate and manage diverse data sources, regardless of their structure. Unlike traditional relational Databases that require rigid schemas, MarkLogic's semi-structured approach allows data to evolve organically without sacrificing data consistency. This is particularly advantageous in scenarios where data requirements may change frequently or where data sources are not standardised. Furthermore, MarkLogic supports the enforcement of schema validation, providing a level of data governance that ensures data integrity and adherence to predefined rules. For industries dealing with regulatory compliance or handling sensitive information, this feature is indispensable, as it ensures that data remains consistent as well as accurate throughout its lifecycle. MongoDB: MongoDB's data model is based on documents stored in JSON-like BSON format. This schema-less design grants developers the freedom to work with data without predefined data structures, making it an excellent choice for agile development environments. It facilitates rapid iteration and adaptation to changing data requirements, which is especially valuable during the early stages of application development. In addition, MongoDB's schema-less approach enables developers to store and process data without conforming to a rigid schema. This can expedite the development process and accommodate dynamic and unstructured data, as commonly found in web and mobile applications. However, the absence of schema validation may lead to data inconsistency if not carefully managed. Query capabilities MarkLogic and MongoDB both have search or query capabilities to handle data. MarkLogic: MarkLogic boasts advanced search capabilities, making it a powerful tool for handling complex and unstructured data. Its built-in search engine employs indexes to efficiently retrieve information and allows for sophisticated search queries, supporting relevancy ranking and facet-based navigation. The search engine can perform full-text searches across different data formats, including text, XML, and JSON, resulting in accurate and relevant search results. Additionally, MarkLogic leverages its semantics capabilities, enabling it to understand the meaning and relationships between different pieces of data. This semantic reasoning empowers users to execute complex queries with greater precision and retrieve insights that might otherwise remain hidden. MongoDB: MongoDB offers a rich set of querying capabilities, providing a flexible and expressive query language to interact with the data. Developers can use various operators, such as $match, $group, and $project, to perform filtering, sorting, and aggregation operations. These capabilities support advanced data manipulation and allow for real-time analytics. However, MongoDB's query performance might degrade in situations involving complex and nested queries due to the absence of semantic indexing. Indexing strategies in MongoDB are essential to ensure efficient query execution, and careful consideration is required when designing data models to avoid performance bottlenecks. Scalability and performance Both MarkLogic and MongoDB have differing scaling capabilities to maintain their performances. MarkLogic: MarkLogic's architecture is designed to scale efficiently both vertically and horizontally. Vertical scaling essentially adds more resources to a single server, while horizontal scaling involves distributing data across multiple nodes. This approach allows MarkLogic to handle large-scale enterprise deployments, ensuring high availability and fault tolerance. With its robust clustering capabilities, MarkLogic can maintain optimal performance even during peak loads and handle substantial workloads. Its smart data distribution mechanisms ensure balanced data distribution across clusters, reducing the risk of performance bottlenecks and improving overall system efficiency. MongoDB: MongoDB is renowned for its horizontal scaling capabilities, which enable it to distribute data across multiple nodes in a cluster. As data volumes grow, organisations can add additional nodes to handle the increasing load, thereby ensuring seamless scalability. This ability to scale horizontally is particularly advantageous for applications experiencing rapid growth and expansion. However, managing data consistency in a distributed environment can be challenging, and careful consideration is necessary to prevent data fragmentation and ensure data integrity. Moreover, MongoDB's scalability relies on efficient shard key selection and shard distribution to avoid hotspots and ensure optimal performance. Master your Cloud Database Skills today by signing up for our Amazon DocumentDB with MongoDB Course! Security MarkLogic and MongoDB both provide essential but differing features to protect data. MarkLogic: MarkLogic prioritises security and provides a comprehensive set of features to protect sensitive data. It offers role-based access control (RBAC), allowing administrators to define user roles and assign specific privileges accordingly. This fine-grained security model ensures that only authorised users can access and modify specific pieces of data. In addition to RBAC, MarkLogic supports encryption at rest, safeguarding data from unauthorised access, even when it is not actively being accessed. These security features make MarkLogic an attractive choice for organisations in data-sensitive industries, such as healthcare, finance, and government, where data protection and regulatory compliance are critical. MongoDB: MongoDB provides essential security features, including authentication, which requires users to provide credentials to access the Database. It also offers access control at the Database level, allowing administrators to define read and write permissions for individual Databases. While these security features are adequate for many use cases, MongoDB may require external security measures, such as firewall configurations or virtual private networks (VPNs), for enhanced data protection. It is crucial for organisations to implement additional security measures as needed to ensure the safety of their data. Use cases Both MarkLogic and MongoDB have different use cases suited to their strengths. MarkLogic: MarkLogic's strengths are particularly well-suited for industries dealing with complex and diverse data types. In the healthcare sector, MarkLogic can efficiently manage electronic health records, medical images, and unstructured clinical notes, while maintaining compliance with data privacy regulations. In the finance industry, MarkLogic's ability to handle complex financial data, including real-time market data and transaction records, makes it an attractive choice for mission-critical applications, such as trading platforms and risk management systems. MarkLogic also excels in the government sector, where it can integrate vast amounts of structured and unstructured data from various sources, empowering decision-makers with comprehensive insights and intelligence. MongoDB: MongoDB's flexible data model and horizontal scalability make it an excellent fit for modern web and mobile applications. It is used in content management systems, e-commerce platforms, and social media applications, where data volumes can quickly grow. Startups and agile development teams often opt for MongoDB due to its ease of use and rapid development capabilities, allowing them to iterate quickly and adapt to evolving data requirements without the constraints of a predefined schema. Furthermore, MongoDB's ability to handle geospatial data effectively opens up opportunities in location-based services, logistics, and IoT applications. Conclusion Choosing between MarkLogic and MongoDB depends on specific project needs, Data Management goals, and scalability requirements. Properly evaluating these aspects will help organisations make an informed decision on which Database solution best aligns with their business objectives. Hope we could provide you with the detailed comparison of MarkLogic vs MongoDB you were looking for!

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