1 The world of the Apache Iceberg Lakehouse

The evolution of data architecture has been shaped by a constant struggle to balance performance, cost, and flexibility while ensuring data remains accessible and governed. Over the years, businesses have cycled through various approaches—data warehouses (analytics-optimized databases), data lakes (analytics on files stored on distributed storage), and hybrid solutions—each attempting to solve the challenges of scaling analytics, reducing complexity, and controlling costs.

1.1 What is a data lakehouse

1.1.1 The rise of data warehouses

1.1.2 The move to cloud data warehouses

1.1.3 The data lake and the Hadoop era

1.1.4 Apache Iceberg: The key to the data lakehouse

1.1.5 The data lakehouse: the best of both worlds

1.2 What is Apache Iceberg?

1.2.1 The need for a table format

1.2.2 How Apache Iceberg manages metadata

1.2.3 Key features of Apache Iceberg

1.2.4 Apache Iceberg as an open-source standard

1.3 The benefits of Apache Iceberg

1.3.1 ACID transactions

1.3.2 Table evolution

1.3.3 Time travel & snapshot-based queries

1.3.4 Hidden partitioning for reduced accidental full-table scans

1.3.5 Cost efficiency & optimized query performance

1.4 The components of an Apache Iceberg lakehouse

1.4.1 The storage layer: The foundation of your lakehouse

1.4.2 The ingestion layer: Feeding data into Iceberg tables

1.4.3 The catalog layer: The entry point to your lakehouse

1.4.4 The federation layer: Modeling & accelerating data

1.4.5 The consumption layer: Delivering value to the business

1.5 Summary