Building a Modern Operational Data Warehouse

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.
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Spotlight

OTHER ON-DEMAND WEBINARS

Redis Ingenuity Webinar Beyond Caching: AI-Powered Search with Redis

Search capability is ingrained into our daily life. How many arguments these days are settled with the conclusion, “Just Google it”??! We all expect some type of search functionality in every application and website. Meanwhile, advances in computer vision, natural language processing, large language models, and generative AI have made it possible to extract semantic properties from unstructured data in the form of vector embeddings. It can be daunting to query this kind of data, which combines K-Nearest Neighbors algorithms and lexical search, unless you have the right tool for the job. When you fail, performance suffers. Web users typically expect search results under one second.
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Why IBM for Sales Performance Management?

FinTech

Improve sales performance and operational efficiencies with better management of incentive compensation plans and smarter administration of sales territories and quotas. Get faster insights with advanced analytics.
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Overcoming Data Management Challenges in AI/ML

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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How to Use data.world to Solve for Common Data Engineering Use Cases

When evaluating a data catalog, every data engineer wants to know one thing: “What’s in it for me?” In this live demo, Mo Dodge answers that question and highlights several high-value use cases data.world solves for:
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