The No Lock-in Apache Iceberg Lakehouse with Agentic Analytics
Published by Self-published · Tech
A practical guide to building a portable Apache Iceberg lakehouse with open catalogs, agentic analytics, and AI-ready data platforms while avoiding vendor lock-in.
Who this book is for
Data engineers, analytics engineers, data architects, platform teams, and technical leaders evaluating Apache Iceberg, open catalogs, federated query, lakehouse migrations, or agentic analytics. It assumes comfort with data platform concepts.
Table of contents
- What is a Data Lakehouse
- What is Apache Iceberg
- Vectors for Lock-in
- Open Source Iceberg Catalogs
- Why Apache Matters When Avoiding Lock-in
- Getting Your Data into Iceberg
- Migrating to Iceberg
- Agentic AI for Apache Iceberg
- Beyond Apache Iceberg
- Tying It All Together
Excerpt
A data team rarely wakes up one morning and decides to build a lakehouse. The usual path is less tidy. First there is a warehouse. It answers the finance dashboard, the executive scorecard, the recurring sales report, and the board deck. It has carefully modeled tables, access controls, and enough structure that people outside the data team can trust what they see. Then the business starts collecting more data than the warehouse was designed to handle comfortably: application logs, clickstreams, partner files, IoT events, document text, audio, images, model features, and raw operational extracts that might matter someday. A data lake appears because object storage is cheap, flexible, and forgiving.
