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The book focuses on designing a complete, modular lakehouse architecture using Apache Iceberg—leveraging open source tools instead of relying on closed, vendor-specific platforms.

Highlights:

• End-to-end guidance on building an Iceberg-based lakehouse from storage to BI
• Integrates tools like Spark, Flink, Dremio, and Polaris
• Covers ingestion pipelines (batch & streaming), schema evolution, governance, and security
• Hands-on examples using PostgreSQL, Apache Superset, and more
• Focus on real-world tradeoffs and implementation decisions at scale

The “lakehouse” data architecture is a powerful way to combine the flexibility of data lakes with the management features of data warehouses. The open source Apache Iceberg framework delivers the scalability, reliability, and performance you want from a lakehouse without the expense and vendor lock-in of platforms like Snowflake, BigQuery, and Redshift.

In Architecting an Apache Iceberg Data Lakehouse , data guru Alex Merced shows you:

• How to create a modular, scalable Iceberg lakehouse architecture
• Where Spark, Flink, Dremio, Polaris fit into your design
• Reliable batch and streaming ingestion pipelines
• Strategies for governance, security, and performance at scale

Apache Iceberg is an open source table format perfect for massive analytic datasets. Iceberg enables ACID transactions, schema evolution, and high-performance queries on data lakes using multiple compute engines like Spark, Trino, Flink, Presto, and Hive. An Iceberg data lakehouse enables fast, reliable analytics at scale while retaining the observability you need for compliance audits, governance, and provable data security.

If you’re exploring Iceberg as an alternative to platforms like Snowflake or BigQuery—or already using it and want to deepen your understanding—this could be a useful resource. The Early Access format also means readers can give feedback as the book evolves.