Vector data lakes and SQL-based AI workflows with Starburst Data
Description
In this session, we explore how Starburst Data enables vector data lakes and SQL-based AI workflows, enabling seamless querying and analysis of high-dimensional data within a familiar SQL interface, while also supporting data mesh architectures for decentralized, domain-driven data ownership and governance.
Abstract
In this session, we explore how Starburst Data enables vector data lakes and SQL-based AI workflows, enabling seamless querying and analysis of high-dimensional data within a familiar SQL interface, while also supporting data mesh architectures for decentralized, domain-driven data ownership and governance. Attendees will learn how vector data lakes enhance AI/ML pipelines, how Starburst's distributed SQL engine simplifies vector search, clustering, and LLM integrations, and real-world use cases where organizations have unified analytics and AI operations. The talk will also cover best practices for optimizing performance at scale, demonstrating how SQL-powered AI can streamline workflows without complex pipelines. Ideal for data engineers, AI practitioners, and analytics leaders, this session highlights the future of vector-enabled analytics and its impact on generative AI and real-time decision-making.
Key Takeaways
- Learn how vector data lakes enhance AI/ML pipelines
- Understand how Starburst's distributed SQL engine simplifies vector search and clustering
- Discover real-world use cases for unified analytics and AI operations
- Master best practices for optimizing performance at scale
- Explore the future of vector-enabled analytics and its impact on generative AI
Speaker
Michael Markieta
Lead Solution Architect, Starburst Data
Michael leads the Solution Architecture practice in the APJ region for Starburst. He brings over a decade of experience ranging from roles as a OpenShift Black Belt at Red Hat, bootstrapping startup co-founder and CTO, and Customer Engineer at Google.