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.