2026-06-08 –, Frannz Salon
Apache Iceberg™ relies on catalogs, but with many options available, choosing the right one is challenging. This session explores the Iceberg catalog landscape, covering core functions, the impact of REST catalogs, and comparisons of Hive, JDBC, Snowflake, and Apache Polaris™ to help you select the best fit for your needs.
Apache Iceberg™ is nothing without its catalog layer; without them, we can’t discover and interact with Iceberg tables or ensure consistency across multiple writers. But with so many Iceberg catalog implementations out there, how can you possibly choose?
In this session, we’ll herd the cat(alogue)s for you and break down the Iceberg catalog landscape. We’ll begin with the basics: why catalogs are essential to Iceberg and the general requirements of a catalog. From there, we’ll examine the introduction of the REST catalog and its impact on Iceberg’s ecosystem. Finally, we’ll survey a number of the most widely used Iceberg catalogs—including HIVE, JDBC, Snowflake, Apache Polaris (incubating) and more––analyzing their metadata storage approaches, table discovery mechanisms, and integration with both open-source and commercial platforms. Along the way, we’ll highlight the strengths, trade-offs, and key considerations for each option.
By the end of this session, you’ll have a clear understanding of how Iceberg catalogs function, what differentiates them, and how to select the best one for your needs—whether you’re optimizing for performance, compatibility, or scalability.
Danica began her career as a software engineer in financial services and pivoted to developer relations, where she focussed primarily on open source technologies under the Apache Software Foundation umbrella such as Apache Kafka and Apache Flink. She now leads the open source advocacy efforts at Snowflake, supporting Apache Iceberg and Apache Polaris (incubating). She can be found on X (Bluesky and Mastodon), talking about tech, plants, and baking @TheDanicaFine.