BigQuery release notes

The default per-project limit of user-specific reservation assignments has been increased from 10 to 100.

BigQuery pipelines now support automated metadata enrichment, allowing you to define semantic metadata directly in SQLX configurations for synchronization with Knowledge Catalog. In addition, the Data Engineering Agent proactively generates this metadata based on your intent or context. For more information, see Metadata enrichment and data quality scorecard integration. This feature is in Preview.

Table Explorer behavior has moved to the Reference panel. Table Explorer has been deprecated. For more information, see "Use the Reference panel" in Run a query.

Query templates for data clean rooms are generally available (GA). Query templates allow data clean room owners and publishers to share predefined queries without exposing the underlying tables and views.

Additionally, table parameters in table-valued functions (TVFs) are generally available (GA). You can use the ANY TABLE type as a table parameter to create generic functions that accept tables of any structure.

BigQuery now supports the gemini-3.1-flash-lite and gemini-3.5-flash GA models, which are available for the us, eu, and global multi-regional endpoints. You can use these models in all generative AI functions. For information about how to specify a multi-regional endpoint and how endpoints are selected, read about locations in the generative AI overview.

You can now use cross-cloud connections to query data in AWS, Azure, and Salesforce Data 360 from all BigQuery regions. These connections let you use more BigQuery features and are more cost efficient than standard connections that use BigQuery Omni. This feature is in Preview.

The JDBC driver for BigQuery now supports OpenTelemetry for tracing and logging, which helps you monitor the performance of your database interactions and troubleshoot issues. Automatic exports to Google Cloud Observability are also available. This feature is generally available (GA).

Support for hybrid search (using the VECTOR_SEARCH function to combine a semantic search with a lexical (keyword) search) has been restored. Using HYBRID mode in the AI.SEARCH function has also been restored.

You can add tables, views, data sources, and data quality tests as tasks to BigQuery pipelines. For more information, see Add a pipeline task. This feature is generally available.

You can audit when users download query results by using the BigQuery console. Data Access audit logs for the tabledata.list method now include a uiDownloadRequest field to indicate whether the request was triggered by a UI download. This feature is generally available (GA).

The BigQuery Data Transfer Service now supports incremental data transfers when transferring data from Klaviyo to BigQuery. This feature is supported in Preview.

You can discover commercial BigQuery sharing listings on Google Cloud Marketplace with the Marketplace filter. For more information, see Subscribe to a Cloud Marketplace-integrated listing. This feature is generally available (GA).

You can use the APPENDS and CHANGES change history functions to view the rows that were appended to or changed in a table during a given time range. This feature is generally available (GA).

You can use the Google-developed Open Database Connectivity (ODBC) driver for BigQuery to connect your applications to BigQuery. This feature is generally available (GA).

The feature formerly known as the legacy tabledata.insertAll method is now called the Storage Write API (REST). The feature formerly known as the Storage Write API is now called the Storage Write API (gRPC).

BigQuery dataset insights is generally available (GA).

BigQuery dataset insights helps you discover and visualize relationships between tables and automatically generate cross-table queries. You can run and publish these insights to Knowledge Catalog for agentic grounding use cases, or generate them on demand without publishing for quick, ad hoc dataset exploration.

An updated version of the Simba ODBC driver for BigQuery is now available.

Lakehouse for Apache Iceberg: 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 and require re-enrollment. Known SAP systems producing these Data Products include SAP Business Warehouse (BW) sources and SAP SuccessFactors.

Lakehouse for Apache Iceberg: Cross-cloud Lakehouse now supports integration with SAP Business Data Cloud (BDC) in Preview.

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 REST catalog (IRC) 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, Query SAP BDC data, and Publish Data Products to SAP BDC.

BigQuery supports data governance tags, which let you enforce column-level security and data masking. Data governance tags are a type of Resource Manager tag that you can attach to sensitive columns and use in BigQuery data policies to grant conditional access to your users. This feature is in Preview.

Conversational analytics now supports the AI.AGG function. This function is in Preview.

As part of Gemini in BigQuery, conversational analytics now supports HIPAA compliance.

Table partitioning, multi-statement transactions, and advanced runtime are now generally available (GA) for Apache Iceberg managed tables.

Cross-cloud Lakehouse now supports Snowflake as a remote catalog provider (Preview). You can configure federated catalogs to query data stored in Snowflake 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.

A Missing Authorization vulnerability was discovered in repositories in BigQuery, Dataform, and Colab Enterprise. An authenticated attacker could potentially escalate permissions and perform cross-tenant repository takeover. For more information, see the GCP-2026-047 security bulletin.

Project caps (also known as scheduling policies) let you limit maximum slots and concurrency per project within a BigQuery reservation. This feature is in Preview.

The BigQuery Overview page is a hub for discovering tutorials, features, and resources to help you get the most out of BigQuery. It provides guided paths for users of all skill levels. This feature is now generally available (GA).

You can use the BigQuery Migration Service MCP server to perform SQL translation tasks, including translating SQL queries into GoogleSQL syntax, generating DDL statements from SQL input queries, and getting explanations of SQL translations.

This feature is Generally Available.

Incremental data transfers for the BigQuery Data Transfer Service for Salesforce are now generally available (GA).

You can now use the ALTER SEARCH INDEX DDL statement to update the configuration of a search index. This feature is in Preview.

You can use the BigQuery Data Transfer Service to transfer metadata from the following data sources into Knowledge Catalog:

• PostgreSQL
• Microsoft SQL Server

This feature is in Preview.

The Data Agent Kit extension is an extension for agent coding tools, such as VS Code, Antigravity, and Cursor, that lets you interact with BigQuery resources directly in your agent environment. You can use this extension to browse datasets, manage pipelines, run queries, and prompt your agent to perform other BigQuery tasks directly in your preferred IDE. This feature is in Preview.

Support for hybrid search (using the VECTOR_SEARCH function to combine a semantic search with a lexical (keyword) search) has been temporarily disabled. We are working to restore this feature as soon as possible.

An updated version of the Simba ODBC driver for BigQuery is now available.

You can now perform multi-level aggregation in GoogleSQL, which lets you use an aggregate function as an argument to another aggregate function. This feature is in Preview.

For data transfers from Facebook Ads, support for the AdInsightsMMM report has been temporarily disabled. Existing data transfers from Facebook Ads that include the AdInsightsMMM report will continue to run, but the transfer won't include data from the AdInsightsMMM report.

This change is due to schema changes in the Facebook Ads API.

For more information, see July 06, 2026.

You can now use pre-trained TimesFM models in BigQuery ML directly from Connected Sheets. These models let you create forecasts and detect anomalies in your data by using the AI.FORECAST and AI.DETECT_ANOMALIES functions. This feature is generally available (GA).

You can specify an optional principal property on BigQuery reservation assignments to route queries to specific reservations based on the identity of the user, service account, or third-party identity executing the job.

In the Google Cloud console, Capacity management has been renamed Workload management.

Effective March 9, 2026, new users are required to have a Cloud Billing account to use the BigQuery Migration Service. This change applies to users starting new projects using BigQuery Migration Service features, such as SQL translation and migration assessment.

After May 18, 2026, all users are required to have a Cloud Billing account to use the BigQuery Migration Service.

Pricing for the BigQuery Migration Service remains without charge.

You can now grant data preparations and pipelines access to additional services when running or scheduling them with user credentials for a Google Account. You can grant data preparations access to Google Drive, and grant pipelines access to Google Drive, Bigtable, and Knowledge Catalog. Extended access options are available in Preview.

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 and Configure table options.

An updated version of the Simba ODBC driver for BigQuery is now available.

You can now use the VECTOR_SEARCH function to combine a semantic search with a lexical (keyword) search. This is known as a hybrid search. For tables with autonomous embedding generation enabled, you can use HYBRID mode in the AI.SEARCH function to simplify your search syntax.

You can also extend a vector index to include keyword information to improve the speed of the lexical search portion of a hybrid search.

This feature is in Preview.

You can now configure your BigQuery pipelines to automatically trigger executions based on updates to specific BigQuery tables. For more information, see Trigger-based scheduling. This feature is in Preview.

Conversational analytics in BigQuery is now generally available (GA) and includes the following features:

• You can select whether an agent can only use generally available models, or a mix of preview and generally available models.
• You can change the thinking mode of an agent within a conversation.
• Agents can ask clarifying questions about your input prompt.
• Agent responses include context citations, to help you understand the specific sources used to generate the answer.
• Parameters are supported in verified queries.

• Agents can use the following AI functions to answer your questions:

  • AI.KEY_DRIVERS
  • AI.IF
  • AI.SCORE
  • AI.CLASSIFY
  • AI.SIMILARITY
  • AI.SEARCH

• Conversational analytics supports US MREP and EU MREP locations that govern the storage of agent and conversation resources, and the location used for ML processing.

You can also create a conversation with a dataset. This feature is in preview.

You can use the BigQuery Data Transfer Service to transfer metadata from the following data sources into Knowledge Catalog:

• Oracle
• MySQL

This feature is in Preview.

You can enable autonomous embedding generation on new or existing tables that you make with the CREATE TABLE or ALTER TABLE statements. When you do this, BigQuery maintains a column of embeddings on the table based on a source column. When you add or modify data in the source column, BigQuery automatically generates or updates the embedding column for that data.

This feature is generally available (GA).

Table Explorer behavior is moving to the Reference panel. This transition will occur in July 2026 or later. For more information, see Table Explorer.

Use Gemini Cloud Assist to analyze your SQL queries and receive recommendations to optimize query performance in BigQuery. This feature is available to customers who use BigQuery editions. This feature is in Preview.

Support for configuring daily token quotas for BigQuery generative AI functions has been temporarily disabled. We are working to restore this feature as soon as possible.

You can resize the width of table columns in BigQuery Studio for BigQuery listings such as datasets, repositories, job history, and connections. To resize a column, hover over the column divider and drag it to your preferred width.

You can use Gemini Code Assist directly within the BigQuery Jobs explorer, Job details, Job history, and Capacity management pages to help you troubleshoot and analyze performance issues. For more information, see Troubleshoot job performance. This feature is in Preview.

Lakehouse for Apache Iceberg now supports custom catalog names and data distributed across multiple buckets. For more information, see Create a catalog.

BigQuery AI functions can use ObjectRef values directly as input, without calling the OBJ.GET_ACCESS_URL function. This feature is generally available (GA).

You can monitor performance, analyze capacity, and optimize costs with Gemini Cloud Assist in BigQuery. This feature is in Preview.

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.

Support for the AI.KEY_DRIVERS function is restored. You can use the AI.KEY_DRIVERS function to identify segments of data that cause statistically significant changes to a summable metric.

This feature is in Preview.

BigQuery continuous queries now support the following aggregation functions:

• ARRAY_AGG
• STRING_AGG

Support for these functions is in Preview.

You can analyze data lineage with Gemini Cloud Assist in BigQuery. This feature is in Preview.

You can now use Gemini Cloud Assist to schedule queries. This feature is in