You can see the latest product updates for all of Google Cloud on the Google Cloud page, browse and filter all release notes in the Google Cloud console, or programmatically access release notes in BigQuery.
April 22, 2026
Cross-cloud Lakehouse is now available in Preview. This feature extends Google Cloud Lakehouse, letting you query data in other cloud providers (such as Databricks Unity Catalog on AWS) directly from Google Cloud using BigQuery, Managed Service for Apache Spark, and Apache Spark without migrating data or complex ETL.
For more information, see About Cross-cloud Lakehouse.
You can now ingest data from external Apache Iceberg REST catalogs (IRC) directly into Google Cloud Lakehouse tables using Dataflow's job builder UI without writing code. For more information, see Import external Iceberg tables into Lakehouse runtime catalog using Dataflow.
You can now add existing Apache Parquet files from cloud-based storage, for example, Cloud Storage or Amazon S3, to an Apache Iceberg table in Google Cloud Lakehouse using the Dataflow job builder. This process registers the files without moving or rewriting the underlying data. For more information, see Import Parquet files in storage into Dataflow.
April 21, 2026
Google Cloud Lakehouse APIs now support Terraform configurations.
Google Cloud Lakehouse gcloud commands for Iceberg namespace and table operations are now Generally Available (GA). Note that the command group remains gcloud biglake.
When querying tables in a catalog with credential vending enabled, BigQuery now uses vended credentials for storage access instead of end-user credentials.
For more information, see Credential vending overview.
April 20, 2026
As of April 20th, 2026, BigLake is now called Google Cloud Lakehouse. BigLake metastore is now called the Lakehouse runtime catalog. Lakehouse APIs, client libraries, CLI commands, and IAM names remain unchanged and still reference BigLake.
As of April 20th, 2026, BigLake is now called Google Cloud Lakehouse. BigLake metastore is now called the Lakehouse runtime catalog. Lakehouse APIs, client libraries, CLI commands, and IAM names remain unchanged and still reference BigLake.
April 16, 2026
Conversational analytics now supports querying Google Cloud Lakehouse tables that connect to the Apache Iceberg REST catalog or are federated to an external catalog.
For more information, see Query Lakehouse data with natural language.
Conversational analytics now supports querying Google Cloud Lakehouse tables that connect to the Apache Iceberg REST catalog or are federated to an external catalog.
For more information, see Query Lakehouse data with natural language.
April 03, 2026
Google Cloud Lakehouse supports Identity and Access Management (IAM) policies at the table level for Apache Iceberg tables (Preview).
For more information, see Manage Lakehouse Apache Iceberg table ACLs.
Google Cloud Lakehouse supports Identity and Access Management (IAM) policies at the table level for Apache Iceberg tables (Preview).
For more information, see Manage Lakehouse Apache Iceberg table ACLs.
March 31, 2026
The Lakehouse runtime catalog Iceberg REST catalogs now support US and EU Cloud Storage multi-region buckets.
When creating a catalog with a US or EU multi-region bucket, you can now
specify the primary location (US or EU) to ensure the catalog is accessible from
the corresponding BigQuery regions. For more information, see
Bucket and catalog
regions.
March 17, 2026
The Lakehouse runtime catalog offers cross-region replication and disaster recovery (Preview) to improve your catalog's availability and resilience.
This feature improves your catalog's availability and resilience by ensuring continuous access, protecting against regional outages, preventing data loss, and enabling failover for Apache Iceberg tables that use the Apache Iceberg REST catalog. For more information, see About cross-region replication and disaster recovery.
The Lakehouse runtime catalog offers cross-region replication and disaster recovery (Preview) to improve your catalog's availability and resilience.
This feature improves your catalog's availability and resilience by ensuring continuous access, protecting against regional outages, preventing data loss, and enabling failover for Apache Iceberg tables that use the Apache Iceberg REST catalog. For more information, see About cross-region replication and disaster recovery.