What you'll learn

Managing metadata across modern data lake engines can quickly become complex and error-prone without a consistent, centralized catalog layer. Inconsistent schemas, duplicated logic, and incompatible engine behavior often lead to fragile pipelines and operational overhead.

In this course, Leveraging Apache Iceberg Catalogs, you’ll gain the ability to configure and use Iceberg catalogs to manage table metadata across Spark, and to use other engines in reliable, engine-agnostic ways.

First, you’ll explore how Iceberg catalogs abstract metadata and enables cross-engine interoperability, starting with local catalog setups and a comparison of JDBC, Hive, and REST implementations.

Next, you’ll discover how to create and register Iceberg tables using SQL and CLI, and understand how metadata is stored and tracked within the catalog.

Finally, you’ll learn how to explore catalog contents, inspect table properties, and perform key operations like renaming and dropping tables—all while observing how the catalog reflects and manages these changes.

When you’re finished with this course, you’ll have the skills and knowledge of Apache Iceberg catalogs needed to build flexible, interoperable data platforms that scale across tools and teams.