This page documents production updates to *Lakehouse*. Check this page for announcements about new or updated features, bug fixes, known issues, and deprecated functionality.
You can see the latest product updates for all of Google Cloud on the
[Google Cloud](https://docs.cloud.google.com/release-notes) page, browse and filter all release notes in the
[Google Cloud console](https://console.cloud.google.com/release-notes),
or programmatically access release notes in
[BigQuery](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=google_cloud_release_notes&t=release_notes&page=table).

## November 02, 2026

Feature *Lakehouse* [caching](https://docs.cloud.google.com/lakehouse/docs/about-cross-cloud-data-access#intelligent-caching)
for cross-cloud data access is
[generally available](https://cloud.google.com/products/#product-launch-stages)
(GA). Cross-cloud caching is disabled by default. You can opt in to caching on
a Lakehouse federated catalog or a BigQuery cross-cloud connection to
cache remote files in your Google Cloud region. This helps reduce
cross-cloud egress costs and accelerate subsequent queries.

For more information, see [About cross-cloud data
access](https://docs.cloud.google.com/lakehouse/docs/about-cross-cloud-data-access#intelligent-caching) and
[Query remote data](https://docs.cloud.google.com/lakehouse/docs/query-remote-data).
Feature *Lakehouse* supports the following remote catalog providers for cross-cloud
data access. This feature is [generally available](https://cloud.google.com/products/#product-launch-stages) (GA).

- [Databricks Unity
  Catalog](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-databricks) on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud
- [AWS Glue Data
  Catalog](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-aws-glue) on AWS
- [Snowflake Horizon
  Catalog](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-snowflake) on AWS, Microsoft Azure, or Google Cloud
- [Workday Data Lake](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-workday) on AWS or Google Cloud

This feature lets you configure federated catalog endpoints to synchronize
catalog metadata and query Apache Iceberg tables in remote catalogs directly
from Google Cloud using BigQuery, Managed Service for Apache Spark, and Apache
Spark without migrating data or building complex ETL pipelines. You can route
background metadata refresh and queries over the public internet or privately
over Cross-Cloud Interconnect, and protect cross-cloud data access with VPC
Service Controls service perimeters. For Databricks Unity Catalog, you can also
use
[user identity
federation](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-databricks#configure-user-identity-federation)
to propagate end-user identities on queries for permission enforcement and audit
logging.

For more information, see [About cross-cloud data
access](https://docs.cloud.google.com/lakehouse/docs/about-cross-cloud-data-access), [Query remote
data](https://docs.cloud.google.com/lakehouse/docs/query-remote-data), and [Use VPC Service Controls with
federated catalog endpoints](https://docs.cloud.google.com/lakehouse/docs/vpc-sc-federated-catalogs).
Feature *Lakehouse* now supports BigQuery change history for
[Apache Iceberg managed tables](https://docs.cloud.google.com/bigquery/docs/biglake-iceberg-tables-in-bigquery).

This feature lets you use the `APPENDS` table-valued function (TVF) to track
rows appended to Apache Iceberg managed tables over a specified time range. You
can also use [BigQuery continuous
queries](https://docs.cloud.google.com/bigquery/docs/continuous-queries) to read incoming data from and write
output rows to Apache Iceberg managed tables.

This feature is [generally available](https://cloud.google.com/products#product-launch-stages)
(GA).

For more information, see [Work with change
history](https://docs.cloud.google.com/lakehouse/docs/change-history) and [Introduction to continuous
queries](https://docs.cloud.google.com/bigquery/docs/continuous-queries-introduction).

## October 14, 2026

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://docs.cloud.google.com/lakehouse/docs/about-spark-hive-metastore) and [Set up Spark and Hive
with the Lakehouse runtime
catalog](https://docs.cloud.google.com/lakehouse/docs/use-spark-hive-metastore).
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://docs.cloud.google.com/lakehouse/docs/migrate-dataproc-metastore).

## October 08, 2026

Feature The following table types now support
[flexible column names](https://docs.cloud.google.com/bigquery/docs/schemas#flexible-column-names) by default:

- [Iceberg external tables](https://docs.cloud.google.com/bigquery/docs/iceberg-external-tables) and [Iceberg managed tables](https://docs.cloud.google.com/bigquery/docs/iceberg-tables) in BigQuery
- [Apache Iceberg tables managed by the Lakehouse runtime catalog](https://docs.cloud.google.com/lakehouse/docs/manage-tables)

This feature is [generally available](https://cloud.google.com/products#product-launch-stages) (GA).

## October 06, 2026

Feature Lakehouse now supports change data capture (CDC)
ingestion from Delta Lake and Apache Iceberg tables
([Preview](https://cloud.google.com/products#product-launch-stages)).

You can now synchronize CDC transaction logs (inserts, updates, deletes) from
Delta Lake and Apache Iceberg tables into Lakehouse
Apache Iceberg tables using Dataflow batch jobs (for Delta Lake)
or batch and streaming jobs (for Apache Iceberg). For more information, see
[Migrate Delta Lake CDC data into Lakehouse using
Dataflow](https://docs.cloud.google.com/lakehouse/docs/migrate-delta-lake-cdc) and
[Migrate Apache Iceberg CDC data into Lakehouse using
Dataflow](https://docs.cloud.google.com/lakehouse/docs/migrate-iceberg-cdc).

## September 28, 2026

Feature You can now write the output rows produced by
[BigQuery continuous queries](https://docs.cloud.google.com/bigquery/docs/continuous-queries-introduction)
directly into [Apache Iceberg managed
tables](https://docs.cloud.google.com/bigquery/docs/biglake-iceberg-tables-in-bigquery). This lets you
continuously process streaming data from BigQuery and write it into your
open-format lakehouse by using an
[`INSERT` DML statement](https://docs.cloud.google.com/bigquery/docs/continuous-queries#write-bigquery).

## September 21, 2026

Feature *Lakehouse* now supports Workday Data Lake as a remote catalog provider
for cross-cloud data access
([Preview](https://cloud.google.com/products#product-launch-stages)). You can
configure federated catalogs to query data stored in Workday Data Lake
directly from Google Cloud using BigQuery or Apache Spark without
migrating data or building complex ETL pipelines.

For more information, see [Set up cross-cloud connection for Workday Data
Lake](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-connection-workday).

## August 31, 2026

Feature *Lakehouse* supports change data capture (CDC) ingestion
(Preview) in regions where the Apache Iceberg REST catalog is supported.

This feature lets you synchronize your source databases with Apache Iceberg
managed tables in real time.

For more information, see [Change data capture
ingestion](https://docs.cloud.google.com/lakehouse/docs/change-data-capture).
Feature *Lakehouse caching* is now available in Preview for borderless Lakehouse.

Lakehouse caching automatically caches data blocks locally within Google Cloud when running cross-cloud queries against remote data (such as AWS S3 or SAP BDC). Caching helps reduce cross-cloud egress fees for repeated query patterns.

For more information, see [Intelligent caching](https://docs.cloud.google.com/lakehouse/docs/about-cross-cloud-data-access#intelligent-caching) and [Monitor cache usage and egress cost savings](https://docs.cloud.google.com/lakehouse/docs/query-remote-data#caching-stats).

## August 19, 2026

Feature You can now import existing Delta Lake tables from Cloud Storage
(`gs://BUCKET_NAME`) directly into
Lakehouse using Dataflow's job builder UI without
writing code. For more information, see [Import Delta Lake tables into
Lakehouse using
Dataflow](https://docs.cloud.google.com/lakehouse/docs/import-delta-lake-tables).

## July 31, 2026

Announcement *Cross-cloud Lakehouse* is now called
*borderless Lakehouse*.

For more information, see [About borderless Lakehouse](https://docs.cloud.google.com/lakehouse/docs/about-borderless-lakehouse).

## July 27, 2026

Feature *Lakehouse* supports the BigQuery Storage Write API (gRPC) (Preview) in
regions where the Apache Iceberg REST catalog is supported.

This feature lets you stream data efficiently using high-performance,
transactionally consistent ingestion into Apache Iceberg managed tables.

For more information, see [Stream data with the
Storage Write API (gRPC)](https://docs.cloud.google.com/lakehouse/docs/bigquery-storage-api).

## July 20, 2026

Issue Data products with special characters, such as "/" or "-", are
not supported and will not be available in BigQuery even if
shared from SAP BDC to BigQuery. If you share a data product with
special characters, this could cause the refresh to stop on the Google side and
require re-enrollment. Known SAP systems producing these data products include
BW sources and SuccessFactors.
Feature *Cross-cloud Lakehouse* now supports integration with SAP
Business Data Cloud (BDC) (GA).

This update includes the following features:

- **Federation from SAP BDC:** Create Delta Sharing catalogs in Lakehouse to automatically synchronize shares, schemas, and tables from SAP BDC.
- **Querying SAP data:** Query synchronized SAP BDC tables directly from BigQuery without data migration.
- **Publishing to SAP BDC:** Publish Apache Iceberg tables or Knowledge Catalog Data Products from Lakehouse directly to SAP BDC, allowing SAP users and applications to consume Google Cloud data directly as remote tables in SAP Datasphere without migrating data.

For more information, see [Set up cross-cloud Lakehouse for SAP
BDC](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-lakehouse-sap-bdc), [Query SAP BDC
data](https://docs.cloud.google.com/lakehouse/docs/query-sap-data), and [Publish Data Products to SAP BDC](https://docs.cloud.google.com/lakehouse/docs/publish-data-to-sap-bdc).

## July 13, 2026

Feature *Cross-cloud Lakehouse* now supports Snowflake Horizon Catalog as a remote
catalog provider (Preview). You can configure federated catalogs to query data
stored in Snowflake Horizon Catalog directly from Google Cloud using
BigQuery or Apache Spark without migrating data or building
complex ETL pipelines.

For more information, see [Set up cross-cloud Lakehouse for
Snowflake Horizon Catalog](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-lakehouse-snowflake).

## June 25, 2026

Feature *Lakehouse* now supports BigQuery writes (DML, DDL) and automatic table
management for Apache Iceberg tables in the Lakehouse runtime catalog
(Preview).

This update introduces the following features:

**Read/write interoperability:** Full read and write (DML) support across
Managed Service for Apache Spark, open source engines (such as Spark,
Flink, and Trino).

**BigQuery SQL DDL and DML:** Create, alter, and delete
tables, or modify data using standard SQL DDL and DML statements
(`INSERT`, `UPDATE`, `DELETE`, and `MERGE`) directly in
BigQuery.

**Automatic table management:** Opt in to automatic storage optimization,
including compaction and garbage collection.

For more information, see [Apache Iceberg tables](https://docs.cloud.google.com/lakehouse/docs/manage-tables)
and [Configure table options](https://docs.cloud.google.com/lakehouse/docs/table-options).

## June 12, 2026

Feature *Lakehouse* now supports custom catalog names and data distributed across
multiple buckets. For more information, see [Create a
catalog](https://docs.cloud.google.com/lakehouse/docs/create-catalog).

## June 11, 2026

Feature *Lakehouse runtime catalog* now supports Apache Iceberg V3 tables with
Binary Deletion Vectors (BDV) (Preview). This feature is useful for handling
high-volume updates and deletes by deleting specific rows without the overhead
of rewriting entire data files.

For more information, see [Use Binary Deletion Vectors in Iceberg V3 tables](https://docs.cloud.google.com/lakehouse/docs/use-iceberg-v3-binary-deletion-vectors).

## June 05, 2026

Feature *Cross-cloud Lakehouse* now supports AWS Glue as a remote catalog
provider (Preview). You can configure federated catalogs to query data stored in
AWS Glue directly from Google Cloud using BigQuery or
Apache Spark without migrating data or building complex ETL pipelines.

For more information, see [Set up cross-cloud Lakehouse for
AWS Glue](https://docs.cloud.google.com/lakehouse/docs/set-up-cross-cloud-lakehouse-aws-glue).

## May 26, 2026

Feature *Lakehouse* now supports using Apache Spark and Apache Hive with
the Lakehouse runtime catalog and the Hive catalog. This integration
lets you use the metastore as a serverless, central metadata layer, enabling
seamless data sharing and unified querying across Spark, Hive, and
BigQuery using supported environments (such as
Managed Service for Apache Spark).

For more information, see [About Hive Catalogs in
Lakehouse runtime catalog](https://docs.cloud.google.com/lakehouse/docs/about-spark-hive-metastore)
and [Use Spark and Hive with the
Lakehouse runtime catalog](https://docs.cloud.google.com/lakehouse/docs/use-spark-hive-metastore).

## May 15, 2026

Feature *Lakehouse* now supports the [BigQuery advanced runtime](https://docs.cloud.google.com/bigquery/docs/advanced-runtime) for Apache Iceberg tables.

For more information, see [BigQuery Iceberg tables overview](https://docs.cloud.google.com/bigquery/docs/biglake-iceberg-tables-in-bigquery).

## May 07, 2026

Feature *Google Cloud Lakehouse* now supports the Apache Iceberg REST [Metrics
Reporting](https://iceberg.apache.org/docs/latest/metrics-reporting/) endpoint.

You no longer have to
[configure your clients](https://docs.cloud.google.com/lakehouse/docs/lakehouse-iceberg-rest-catalog#configure-catalog)
to use the `rest-metrics-reporting-enabled=false` option.

## April 22, 2026

Feature *Cross-cloud Lakehouse* is now available in Preview. This feature
extends *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](https://docs.cloud.google.com/lakehouse/docs/about-cross-cloud-lakehouse).
Feature 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
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
Lakehouse runtime catalog using
Dataflow](https://docs.cloud.google.com/lakehouse/docs/blms-import-parquet).
Feature You can now ingest data from external Apache Iceberg REST catalogs (IRC)
directly into *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](https://docs.cloud.google.com/lakehouse/docs/blms-import-iceberg).

## April 21, 2026

Announcement *Lakehouse* `gcloud` commands for Iceberg namespace and table
operations are now Generally Available (GA). Note that the command group remains
`gcloud biglake`.
Feature *Lakehouse* APIs now support Terraform configurations.
Feature 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](https://docs.cloud.google.com/lakehouse/docs/credential-vending).

## April 20, 2026

Announcement As of April 20, 2026, BigLake is now called *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

Feature Conversational analytics now supports querying *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](https://docs.cloud.google.com/lakehouse/docs/conversational-analytics).

## April 03, 2026

Feature *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](https://docs.cloud.google.com/lakehouse/docs/manage-tables-acl).

## March 31, 2026

Feature 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](https://docs.cloud.google.com/lakehouse/docs/about-iceberg-rest-catalog-endpoint#bucket_and_catalog_regions).

## March 17, 2026

Feature *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](https://docs.cloud.google.com/lakehouse/docs/about-managed-disaster-recovery).