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Databricks Unveils APIs to Prevent Iceberg Catalog Lock-In

Databricks has contributed a new UNREGISTER API to the Apache Iceberg specification, allowing organizations to migrate tables between catalogs without risking data loss or copying files.

Databricks AI1 day agoBusiness
Image: Databricks AI

Databricks has introduced support for the REGISTER and UNREGISTER APIs within its Unity Catalog, currently available in private preview. By contributing the new UNREGISTER endpoint to the Apache Iceberg REST catalog specification, the company aims to solve a persistent challenge in lakehouse architectures: the difficulty of moving open table formats between different catalogs without duplicating underlying data or risking system conflicts.

In a standard lakehouse setup, catalogs coordinate table commits and track metadata locations in object storage. While the existing REGISTER API allowed users to point a new catalog to an existing table's metadata, there was no safe way to remove the table from the old catalog. Running a traditional DROP TABLE command would delete both the metadata and the actual data files. Without a clean way to sever the connection, administrators faced split-brain scenarios where two independent catalogs attempted to manage the same table, resulting in silent data corruption and out-of-sync query results.

The newly introduced UNREGISTER API addresses this gap by safely removing a table's entry from the source catalog without altering any underlying files in object storage. When triggered via an empty POST request to the table's resource endpoint, the API returns the exact metadata pointer required by the destination catalog. The administrator can then pass this pointer directly to the REGISTER endpoint of the target catalog, completing a seamless handoff that ensures only one catalog maintains commit authority at any given time.

For data engineers and database administrators, this development eliminates catalog lock-in and simplifies architectural migrations. Instead of executing costly data-copying operations or risking data loss, teams can now safely transfer table management across different engines like Spark and Trino. Databricks is offering these capabilities in private preview for Unity Catalog users, who must contact their account teams to test the integration.

This is our own summary of reporting by Databricks AI

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