HIGH-PERFORMANCE INTERACTIVE SQL ANALYTICS AND MACHINE LEARNING ON HADOOP

The criticality of becoming a data-driven enterprise is a given. But there are multiple data, security and governance approaches to get there. Which should you choose? Learn the challenges of implementing a Data Lake and the benefits of a modern Data Mesh approach with AWS. Hear from AWS Senior Partner Solutions Architect, Ayan Ray, and Privacera Director of Sales Engineering, Lovelesh Chawla, as they discuss the importance of implementing a modern unified data security governance platform. Manage, secure, and automate data access, security, and compliance simply and consistently across your entire data mesh on AWS.

For ‘code-first’ data scientists notebooks are an essential part of your toolkit, but they have serious limitations when they’re run locally on laptops. In this informative session we will outline how DataRobot’s latest acquisition, Zepl, brings the power of self-service and cloud-hosted notebooks to you and your team.

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.

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.