BigQuery Lakehouse Hive catalog endpoint and Dataproc Metastore migration generally available

The Apache Hive catalog endpoint in the Lakehouse runtime catalog is now generally available (GA). This integration lets you use the metastore as a serverless, central metadata layer, enabling seamless data sharing and unified querying across Apache Spark, Apache Hive, and BigQuery.

For more information, see About the Hive catalog endpoint and Set up Spark and Hive with the Lakehouse runtime catalog.

Metadata migration from Dataproc Metastore to Lakehouse is now generally available (GA). You can perform a one-time backfill migration or dry run to migrate metadata from a Dataproc Metastore service to an Apache Iceberg REST catalog endpoint or a Hive catalog endpoint using the Google Cloud console, the gcloud CLI, or the REST API.

For more information, see Migrate metadata from Dataproc Metastore to Lakehouse.

Conversational analytics now lets you create a conversation with all of the data sources in multiple projects, or create a conversation with a dataset. These features are generally available (GA).

You can publish a BigQuery Conversational Analytics agent in Gemini Enterprise. This feature is now generally available (GA).

The AI.AGG function now supports the output_schema argument, which lets you specify the schema of the function output as a comma-separated list of fields. This feature is generally available (GA).