AI and the Lakehouse: How Starburst is Pioneering New Workflows
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Summary In this episode of the Data Engineering Podcast Alex Albu, tech lead for AI initiatives at Starburst, talks about integrating AI workloads with the lakehouse architecture. From his software engineering roots to leading data engineering efforts, Alex shares insights on enhancing Starburst's platform to support AI applications, including an AI agent for data exploration and using AI for metadata enrichment and workload optimization. He discusses the challenges of integrating AI with data systems, innovations like SQL functions for AI tasks and vector databases, and the limitations of traditional architectures in handling AI workloads. Alex also shares his vision for the future of Starburst, including support for new data formats and AI-driven data exploration tools. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. 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Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Your host is Tobias Macey and today I'm interviewing Alex Albu about how Starburst is extending the lakehouse to support AI workloads Interview Introduction How did you get involved in the area of data management? Can you start by outlining the interaction points of AI with the types of data workflows that you are supporting with Starburst? What are some of the limitations of warehouse and lakehouse systems when it comes to supporting AI systems? What are the points of friction for engineers who are trying to employ LLMs in the work of maintaining a lakehouse environment? Methods such as tool use (exemplified by MCP) are a means of bolting on AI models to systems like Trino. What are some of the ways that is insufficient or cumbersome? Can you describe the technical implementation of the AI-oriented features that you have incorporated into the Starburst platform?What are the foundational architectural modifications that you had to make to enable those capabilities? For the vector storage and indexing, what modifications did you have to make to iceberg?What was your reasoning for not using a format like Lance? For teams who are using Starburst and your new AI features, what are some examples of the workflows that they can expect? What new capabilities are enabled by virtue of embedding AI features into the interface to the lakehouse? What are the most interesting, innovative, or unexpected ways that you have seen Starburst AI features used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI features for Starburst? When is Starburst/lakehouse the wrong choice for a given AI use case? What do you have planned for the future of AI on Starburst? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links StarburstPodcast Episode AWS Athena MCP == Model Context Protocol LLM Tool Use Vector Embeddings RAG == Retrieval Augmented GenerationAI Engineering Podcast Episode Starburst Data Products Lance LanceDB Parquet ORC pgvector Starburst Icehouse The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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