The Apache Hive catalog endpoint in the Lakehouse runtime catalog is now

## Feature Feature The Apache Hive catalog endpoint in the Lakehouse runtime catalog is now[generally available](https://cloud.google.com/products#product-launch-stages) (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](https://cloud.google.com/lakehouse/docs/about-spark-hive-metastore) and [Set up Spark and Hive with the Lakehouse runtime catalog](https://cloud.google.com/lakehouse/docs/use-spark-hive-metastore). ## Feature Feature Metadata migration from Dataproc Metastore to Lakehouse is now [generally available](https://cloud.google.com/products#product-launch-stages) (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](https://cloud.google.com/lakehouse/docs/migrate-dataproc-metastore). ## Feature Feature Conversational analytics now lets you[create a conversation](https://cloud.google.com/bigquery/docs/create-conversations#converse%5Fwith%5Fa%5Fdata%5Fsource%5Fusing%5Fthe%5Fagents%5Fpage)with all of the data sources in multiple projects, or create a conversation with a [dataset](https://cloud.google.com/bigquery/docs/create-conversations#datasets). These features are[generally available](https://cloud.google.com/products#product-launch-stages)(GA). ## Feature Feature You can publish a [BigQuery Conversational Analytics agent in Gemini Enterprise](https://cloud.google.com/bigquery/docs/create-data-agents#publish-agent-gemini-enterprise). This feature is now [generally available](https://cloud.google.com/products/#product-launch-stages) (GA). ## Feature Feature The[AI.AGG function](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-agg)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](https://cloud.google.com/products#product-launch-stages)(GA).