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Everything From Data Tips and NoSQL Best Practices to Industry Specific Insights

 
Data Hub Central makes it easy to discover and use your data through the built in Explore interface. And in the case of the example shown in this tutorial, because of how we modeled the Customer entity and curated the data, simply providing our Data Analysts access to the Explore interface will enable their requirements […]
 
Financial services firms around the world that buy, sell and use investment research face a converging series of challenges that are placing a premium on better research data to differentiate insights and outperform the competition. Unfortunately, the ability to rapidly unify and access the heterogeneous data needed to generate advanced analytics and unique insights is […]
 
Drew talks through a quick example of semantic search using triples and document metadata created in a MarkLogic Data Hub. He covers loading RDF triples, modeling canonical entities, Data Hub flows, SPARQL queries, and multi-model searching with Optic API.
 
In this short demo, Damon Feldman shows off an app built on MarkLogic Data Hub Service to help researchers get new insights into coronavirus (COVID-19) using a graph based search capability with built-in machine learning and AI.
 
DuPont is a science-based company with over 200 years of heritage and a diverse business portfolio in nutrition, biotechnology, electronics, transportation, safety, and construction. The combination of legacy and diversity has resulted in significant data challenges, ranging from incompatible legacy systems, data fragmentation, to loss of institutional knowledge through attrition. In this session, you’ll learn […]
 
In this video, Frederic Decaudin explains what MLTV is and how he and his team built it. Fred is a Senior Director of Solutions Engineering at MarkLogic and a chief architect of MLTV. The app shows many capabilities of MarkLogic Data Hub Service such as full-text search and semantics.
 
Learn how to find and understand query plans for MarkLogic SQL, SPARQL, and Optic API queries. Gain a foundation for understanding efficient query execution in the Optic Engine. Part 1 of a 5-part deep dive into MarkLogic’s Optic Engine.
 
Learn how the optimizer inside MarkLogic’s Optic Engine works. Examine topics like how to write where expressions that can be pushed to the D-nodes, and what different optimization levels are useful for. Build on your understanding of efficient query execution in the Optic Engine. Part 2 of a 5-part deep dive into MarkLogic’s Optic Engine.
 
Understand and diagnose performance problems in MarkLogic SQL, SPARQL, and Optic API queries. Find and analyze logging and trace output for query optimization and execution. Part 3 of a 5-part deep dive into MarkLogic’s Optic Engine.
 
Learn how the structure of MarkLogic’s Triple Index provides data compression, and how it affects query performance. Understand the difference between triples and triple values, and the block structure of the data on disk. Gain a foundation for understanding efficient Triple Index queries. Part 4 of a 5-part deep dive into MarkLogic’s Optic Engine.
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