Iceberg for Agents: Turning Lakehouse Data into AI Context
Details
AI agents fail in production because they're overwhelmed with data but starved for context. LLM models aren't the problem. The bottleneck is the data stack: fragmented silos, inconsistent definitions, and logic hidden in tribal knowledge. Agents need structured, reliable, and interpretable context—not just data access.
In this session, we'll show how Apache Iceberg becomes the backbone of AI-ready pipelines. You'll learn how to elevate your Iceberg implementation from a storage format to a live context layer that powers structured retrieval-augmented generation (RAG), schema-aware agents, and autonomous reasoning grounded in truth.
What we'll cover:
- Iceberg Foundations for AI - from ACID to Time Travel
- From Rows to Relationships - The role of the semantic layer
- Structured RAG in Practice - Fully open source
The session includes a live demo of a fully open-source Structured RAG stack built on Apache Iceberg, featuring semantic query translation, hybrid retrieval, and governed agent reasoning. Expect architecture diagrams, real code, and practical guidance.
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## SPEAKER BIO
Andrew Madson
Head of Developer Relations, Fivetran | Data & AI Evangelist
Andrew is a Data & AI Evangelist, Keynote Speaker, Open Source Advocate, and Professor who transforms the complex into the accessible. His insights combine multiple master's degrees in data analytics and business management with hands-on leadership experience at enterprise organizations.
His technical expertise spans data analytics, machine learning, and artificial intelligence, complemented by strong communication skills developed through roles as an adjunct instructor at six universities and keynote speaker. He bridges the gap between technical innovation and practical application, helping organizations unlock the full potential of their data.