Apache Iceberg for Agentic AI: Connecting Structured Enterprise
Published by Self-published · Tech
Explores how to connect structured enterprise data in Apache Iceberg lakehouses to agentic AI systems.
Who this book is for
Data engineers who build Apache Iceberg pipelines but rarely think about how agents will use their tables, and AI engineers who build agent workflows but struggle to connect them to governed, structured enterprise data.
Table of contents
- Why Agents Need Structured Data
- Apache Iceberg Fundamentals for Agent Workloads
- Designing Agent-Ready Iceberg Tables
- Catalogs and Metadata for Agent Discovery
- The Semantic Layer: Giving Data Meaning for Agents
- MCP: Connecting Agents to Iceberg Data
- Query Federation: Agents Across Data Sources
- AI SQL Functions and In-Query Intelligence
- Building Agentic Pipelines on Iceberg
- Governance and Security for Agent Data Access
- Performance Optimization for Agent Queries
- Reference Architecture and Migration Guide
Excerpt
The era of large language models began with documents. We taught models to read web pages, summarize PDFs, and converse in natural language based on immense corpora of unstructured text. When developers started building autonomous agents, they naturally gravitated toward the same domain. Retrieve-and-generate (RAG) pipelines became synonymous with searching a vector database of text snippets. But the most critical information in any enterprise does not live in text documents. It lives in structured tables. Revenue metrics, inventory counts, user clickstreams, and financial transactions are highly structured datasets, carefully curated and stored in data lakehouses.
