BigQuery release notes

Conversational analytics in BigQuery now supports the ML.CORRELATION function to calculate statistical correlations between a target column and one or more metric columns in a table. This feature is in Preview.

You can now use the ML.METRICS function to compute evaluation metrics for machine learning classification or regression tasks on any table or query that contains actual and predicted values. This function lets you evaluate predictions without needing to create or reference a stored model. This feature is in Preview.

You can use the AI.CAUSAL_EFFECT function to quantify the impact of specific interventions on time series data. This feature is in Preview.

BigQuery generative AI functions now support the following Gemini models:

• gemini-3.5-flash-lite
• gemini-3.6-flash
• gemini-3.7-flash

Conversational analytics now supports predictive modeling questions using the AI.PREDICT function. This feature is in Preview.

Conversational analytics now supports questions about market basket analysis. This feature is generally available (GA).

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

The Rust SDK for BigQuery is now in Preview.

You can now create, store, and manage pipelines in Git folders. This feature is in Preview.

BigQuery now supports TabFM, Google's pre-trained foundation model for tabular data. TabFM enables zero-shot regression and classification through in-context learning. It delivers high-accuracy predictions on structured data without requiring model training or hyperparameter tuning. You can use TabFM directly through the AI.PREDICT function and evaluate model performance by using the AI.EVALUATE function. This feature is in Preview.

Support for configuring daily token quotas for BigQuery generative AI functions has been restored.

Using folders to create, store, and manage pipelines is generally available.

You can now create an identity column, sometimes referred to as an auto-incrementing column, on a table. You can use identity columns to create and maintain primary keys on your tables. When you insert a row into a table that has an identity column, BigQuery generates a unique integer value for that column. This feature is in Preview.

BigQuery Graph is generally available (GA).

BigQuery Graph now includes support for the CALL graph query statement, and the following path inspection GQL functions:

• IS_ACYCLIC
• IS_SIMPLE
• IS_TRAIL.

The Data Engineering Agent is now HIPAA compliant. For more information, see HIPAA compliance on Google Cloud.

Core graph processing for BigQuery Graph requires an Enterprise or Enterprise Plus edition reservation. Existing allowlisted users can continue to use Standard edition or on-demand billing until April 26, 2027, after which these billing models will no longer be supported for core graph processing.

Graph measures will remain available in the Enterprise and Enterprise Plus editions and for queries run using on-demand pricing. Measures are not available in Standard edition.

You can now train models using XGBoost version 2.1 by using the XGBOOST_VERSION option. BigQuery uses the XGBoost 3.1.0 library to load and make predictions on XGBoost models. This feature is generally available.

An Improper Input Validation vulnerability was discovered in the JDBC driver in BigQuery Data Transfer Service versions prior to May 1, 2026. An authenticated attacker could use crafted JDBC connection string parameters to achieve remote code execution in the connector container and escalate privileges in the tenant project. For more information, see the GCP-2026-056 security bulletin.

You can now view real-time logs for your Python UDFs in Cloud Logging. This feature is generally available.

BigQuery data governance tags are supported in Terraform. This feature is in Preview.

You can now monitor the performance, adoption, latency, and costs of your data agents and their conversations by using Google Cloud Observability in BigQuery. This feature is in Preview.

BigQuery supports the following table-valued functions in the query editor and as part of conversational analytics to help you analyze your time series data:

• ML.TREND: identify the directional trajectory of your data.
• ML.SEASONALITY: identify repeating patterns in your data.
• ML.DETECT_CHANGE_POINTS: identify intervals where structural shifts occur in your data.

These functions are in preview.

Starting April 26, 2027, core graph processing for BigQuery Graph will be restricted to the BigQuery Enterprise and Enterprise Plus editions. Consequently, we are deprecating support for Standard edition and on-demand billing for core graph processing.

Graph measures will remain available in the Enterprise and Enterprise Plus editions and for queries run using on-demand pricing. Measures are not available in Standard edition.

For additional details and enforcement dates regarding Standard edition and on-demand billing support, see the August 27, 2026 release note.

The run_bq_command tool exposes the bq command-line tool within the Cloud CLI remote MCP server. AI agents can now execute advanced BigQuery operations, such as job scheduling, job management, and reservation management, through a managed MCP endpoint. For more information, see Use the Cloud CLI remote MCP server. This feature is in Preview.

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