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 Preview.

You can use the Google-developed, open source Java Database Connectivity (JDBC) driver for BigQuery to connect your Java applications to BigQuery. This feature is generally available (GA).

You can use custom constraints with Organization Policy to provide more granular control over specific fields for some BigQuery sharing resources. For more information, see Manage Sharing data exchanges and listings using custom constraints. This feature is generally available (GA).

IAM deny policies for BigQuery are now generally available (GA).

You can manage and limit the costs associated with BigQuery generative AI functions by configuring daily token quotas. Token-based cost management for BigQuery generative AI functions is generally available (GA).

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.

BigQuery fluid scaling, which provides per-second billing with no minimum duration for autoscaling reservations, is generally available (GA).

Remote functions now support a custom path in the endpoint URL. You can reuse a single Cloud Run service for multiple BigQuery remote functions by specifying different path suffixes on the same endpoint. This feature is generally available (GA).

The Facebook Ads connector for the BigQuery Data Transfer Service now supports data transfers from the following Facebook Ads reports:

• AdInsightsMMM
• Ads
• AdCreatives
• AdSets
• Campaigns
• AdImages
• AdLabels
• Businesses
• CustomAudiences

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

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 and Use Spark and Hive with the Lakehouse runtime catalog.

The Data Science Agent (DSA) for Colab Enterprise and BigQuery is now generally available (GA).

BigQuery can re-execute instructions (queries) to try to proactively detect performance, correctness, or functional regressions. These re-executions will have no side effects and will happen with no additional cost or resource consumption. Data access logs may show bigquery-adminbot@system.gserviceaccount.com when BigQuery re-executes an instruction.

Python UDFs are now Generally Available (GA).

You can use Python UDFs to implement a scalar function in Python and use it in a SQL query. Python UDFs let you install third-party libraries from the Python Package Index (PyPI) and let you access external services using a Cloud resource connection.

You can now use the AI.AGG function to semantically aggregate unstructured input data based on natural language instructions. This feature is in Preview.

You can group reservations together to prioritize idle slot sharing within the group. Reservations within a reservation group share idle slots with each other before making them available to other reservations in the project, giving you more control over slot allocation for high-priority workloads. This feature is generally available (GA).

You can now use a custom organization policy to allow or deny specific operations on workload management resources including reservations, assignments, capacity commitments, and BI reservations. This feature is in Preview.

You can manage and version control SQL scripts and notebooks with BigQuery Studio Git repositories, which provide a streamlined, folder-based integration with remote Git repositories. This feature is in preview.

The AI.DETECT_ANOMALIES function supports calling the function with a single input table that holds both the historical and target data. This feature is generally available (GA).

Support for the AI.KEY_DRIVERS function preview has been temporarily disabled. We are working to restore this feature as soon as possible.

You can now use the AI.COUNT_TOKENS function to estimate the token count of text input that you provide. For some generative AI functions, you can view the total number of input, output, thought, and cache tokens for each modality processed by the query. These features are in Preview.

Starting August 11, 2026, the billing label for the BigQuery Data Transfer Service SKU will be updated from goog-bq-feature-type: DATA_TRANSFER_SERVICE (uppercase) to goog-bq-feature-type: data_transfer_service (lowercase) to provide a more unified and complete view of your costs. This update expands the scope of the label to cover all costs associated with the BigQuery Data Transfer Service, including data transfer orchestration, data load operations, and data merge operations.

To ensure uninterrupted cost visibility, update your billing exports, dashboards, and reporting queries to include both these labels.

You can configure BigQuery sharing listings for multiple regions, which allows you to share datasets and linked replicas across global geographies simultaneously. For more information, see Create a listing. This feature is generally available (GA).

Starting June 1, 2026, due to changes in Google Ads data retention policies, the BigQuery Data Transfer Service connectors for Google Ads, Search Ads 360, and Google Analytics 4 will stop populating data for backfill runs with dates earlier than 37 months from the current date.

For more information about the changes to the Google Ads data retention policies, see New Data Retention Policy for Google Ads starting June 1, 2026.

Starting May 7, 2026, new transfer configurations that transfer data from Google Ads using the BigQuery Data Transfer Service will require Multi-factor authentication (MFA) for individual user authentication. For more information, see May 7, 2026.

Strict act-as mode is enforced globally for all Dataform repositories, requiring the use of a custom service account or user credentials for running Dataform workflows, BigQuery pipelines, notebooks, and data preparations.

You can now use the VECTOR_INDEX.STATISTICS function to calculate how much an indexed table's data has drifted between when a vector index was created and the present. If table data has changed enough to require a vector index rebuild, you can use the ALTER VECTOR INDEX REBUILD statement to rebuild the vector index without downtime. These features are generally available (GA).

You can now use the PARTITION BY clause of the CREATE VECTOR INDEX statement to partition TreeAH vector indexes. Partitioning enables partition pruning and can decrease I/O costs. This feature is Generally Available.

You can now create materialized views over active change data capture (CDC) enabled tables. This feature is generally available (GA).

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

You can now use the visual graph modeler in BigQuery Studio to define BigQuery graph nodes and edges from your BigQuery tables and edit graph schema. This feature is available in Preview.

Dataproc is now called Managed Service for Apache Spark. The names for associated API, client library, CLI, and Identity and Access Management (IAM) resources remain unchanged.

BigLake is now called Google Cloud Lakehouse. BigLake metastore is now called the Lakehouse runtime catalog. The names for associated APIs, client libraries, CLI commands, and Identity and Access Management (IAM) remain unchanged and still reference BigLake.

Dataplex Universal Catalog is now called Knowledge Catalog. The API, client library, CLI, and Identity and Access Management (IAM) names remain unchanged. For more information, see Knowledge Catalog overview.

Looker Studio is now called Data Studio. The website and endpoint change from lookerstudio.google.com to datastudio.google.com. You do not need to update your reports for this change, as Data Studio automatically redirects to the new domain. However, if your company uses proxies to restrict access to external sites, your IT administrator needs to add the new domain to your access control list (ACL). The names for associated API, client library, CLI, and Identity and Access Management (IAM) resources remain unchanged. For more information, see Data Studio returns as new home for Data Cloud assets.

BigQuery graphs now support the following features:

• You can query graphs using natural language in Conversational Analytics.
• You can add descriptions and synonyms to the labels and properties in your graphs.
• For some types of graphs you can define measures, which lock an aggregation to a key to help you perform complex aggregations without overcounting. To query measures, you transform your graph into a flattened table by using the GRAPH_EXPAND TVF, and then query measures in that table with the AGG function.

These features are in Preview.

You can now use the Data Engineering Agent to build, modify, and troubleshoot data pipelines in BigQuery. This feature is generally available (GA).

You can now use the gemini-embedding-2-preview model in the AI.EMBED, AI.SIMILARITY, and AI.GENERATE_EMBEDDING functions to generate a single embedding from a combination of input types, including text, image, audio, video, and PDF files. This feature is in Preview.

You can now visualize BigQuery graph query results and graph schemas directly in BigQuery Studio, without the need of a notebook environment. This feature is in Preview.

Starting July 25, 2026, the BigQuery Data Transfer Service for Facebook Ads connector will update the data type mapping for the ActionValue field in the AdInsightsActions report from INT to FLOAT.

The following features have been added to Python UDFs during Preview:

• Vectorized UDFs with Apache Arrow. You can now create vectorized Python UDFs using the Apache Arrow RecordBatch interface for improved performance.
• Cloud Monitoring integration. Python UDFs now export metrics to Cloud Monitoring, including CPU utilization, memory utilization, and maximum concurrent requests per instance.
• Container request concurrency. A new option, container_request_concurrency, is available for the CREATE FUNCTION statement. This option controls the maximum number of concurrent requests per Python UDF container instance.
• New quotas. Python UDFs are now subject to new quotas on image storage bytes (10 GiB per project per region) and mutation rate (30 per minute per project per region).
• Cost visibility. Python UDF costs can be seen in the external_service_costs column in the INFORMATION_SCHEMA.JOBS view and in the ExternalServiceCosts field in the Job API.

You can now migrate metadata from external data catalogs to BigLake tables for Apache Iceberg. This feature supports external data catalogs such as such as Apache Hive Metastore and Apache Iceberg REST Catalog. This feature is in Preview.

You can use the BigQuery MCP server to perform a range of data-related tasks with your AI applications including:

• Examining BigQuery resources.
• Generating accurate and efficient SQL queries.
• Securely executing queries.
• Interpreting query results.

This feature is Generally Available (GA).

You can now publish a BigQuery Conversational Analytics agent in Gemini Enterprise. This feature is in Preview.

You can now use the notebook gallery in the BigQuery web UI as your central hub for discovering and using prebuilt notebook templates. This feature is generally available (GA).

Using folders to organize and control access to single file code assets is generally available (GA). In addition, you can perform bulk move and delete operations, refresh folder contents, and view full breadcrumb paths based on resource permissions. For more information, see Create and manage folders.

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 BigLake data with natural language.

This feature is in Preview.

You can now use Colab Data Apps to transform your data analyses from Colab notebooks into polished, interactive applications.

This feature is in Preview.

You can now 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 Apache Iceberg external tables now support Iceberg version 3, including binary deletion vectors. For more information, see Apache Iceberg external tables. This feature is in Preview.

A known issue has been resolved where a materialized view refresh could expose masked or filtered data from fine grained access control policies in error messages. No further action is needed.

BigQuery agent analytics is now generally available (GA) in the Google Agent Developer Kit. BigQuery agent analytics is an open source solution that lets you capture, analyze, and visualize multimodal agent interaction data at scale.

You can now use EXPORT DATA statements to reverse ETL BigQuery data to AlloyDB. This feature is in Preview.

Support for the AI.AGG function preview has been temporarily disabled. We are working to restore this feature as soon as possible.

To reduce LLM token consumption and query latency when processing large datasets, enable optimized mode using the following managed AI functions:

• AI.IF
• AI.CLASSIFY

This feature is in Preview.

The following managed AI functions use Gemini to help you filter, join, rank, and classify your data:

• AI.IF: Filter and join text and unstructured data (such as images, PDFs, audio, or video) based on a condition described in natural language.
• AI.SCORE: Rate text and unstructured data (such as images, PDFs, audio, or video) to rank your data by quality, similarity, or other criteria.
• AI.CLASSIFY: Classify text and unstructured data (such as images, PDFs, audio, or video) into user-defined categories.

These functions are generally available (GA).

You can use visualization cells to automatically generate a visualization of any DataFrame in your notebook. You can customize the columns, chart type, aggregations, colors, labels, and title.

This feature is generally available (GA).

SQL cells in BigQuery notebooks are now generally available (GA).

The BigQuery Data Transfer Service can now transfer data from Snowflake to BigQuery. This feature is generally available (GA).

You can now use stateful operations in continuous queries, which let you perform complex analysis by retaining information across multiple rows or time intervals using JOINs and windowing aggregations. This feature is in Preview.

You can now use BigQuery Graph to model your data as a graph and perform analysis on a large scale.

• Create a graph directly from tables that store entities and relationships between entities. You don't need to modify your existing workflows or replicate your data to use it in graph queries.

• Use Graph Query Language (GQL) to find complex, hidden relationships between data points that would be challenging to find using SQL.

• Visualize your graph schema and graph query results in a notebook.

This feature is in Preview.

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

You can now use the @@session_id system variable with SQL user-defined functions, table functions, and logical views. This feature is generally available (GA).

The BigQuery Data Transfer Service now supports incremental data transfers for the following data source connectors:

• MySQL
• Oracle
• PostgreSQL
• ServiceNow

These features are supported in Preview.

You can now use the built-in text embedding model embeddinggemma-300m in the AI.EMBED and AI.SIMILARITY functions. This model uses your BigQuery slots to generate embeddings at scale. This feature is in Preview.

You can now use the AI.AGG function to semantically aggregate unstructured input data based on natural language instructions. This feature is in Preview.

You can now use a custom organization policy to allow or deny specific operations on these BigQuery resources: tables, data policies, and row access policies. This feature is in preview.

You can now use the CREATE CONNECTION, ALTER CONNECTION SET OPTIONS, and DROP CONNECTION data definition language (DDL) statements to manage Cloud resource connections with GoogleSQL. Additionally, you can now use the connection user type and PROJECT resource type with GRANT and REVOKE data control language (DCL) statements to manage connection and project access. These features are generally available (GA).

The BigQuery Migration Service supports SQL translations from Snowflake SQL to GoogleSQL. This feature is now generally available (GA).

With this change, the translation service supports a wider variety of Snowflake SQL and has improved support for several data types. Among other changes, the translation service maps Snowflake INTEGER and zero-scale NUMERIC types up to precision 38 to INT64 type in GoogleSQL for improved performance by default.

You can set the column granularity when you create a search index, which stores additional column information in your search index to further optimize your search query performance. This feature is generally available (GA).

BigQuery ObjectRef values now support the following:

• You can run ObjectRef functions with either direct access or delegated access.
• The OBJ.MAKE_REF function automatically fetches the latest Cloud Storage metadata and populates this in the ref.details field.
• The OBJ.GET_READ_URL function returns a STRUCT value with a read URL and status columns and renders image results in the Cloud console. Use this function when you don't require a write URL.

These features are generally available (GA).

The following forecasting and anomaly detection functions and updates are generally available (GA):

• The AI.DETECT_ANOMALIES function supports providing a custom context window that determines how many of the most recent data points should be used by the model.

• The AI.FORECAST function supports specifying the latest timestamp value for forecasting.

• The AI.EVALUATE function supports the following:

  • You can provide a custom context window that determines how many of the most recent data points should be used by the model.

  • The function outputs the mean absolute scaled error for the time series.

You can now create BigQuery non-incremental materialized views over Spanner data to improve query performance by periodically caching results. This feature is generally available (GA).

You can now use Cloud resource connections with EXPORT DATA statements to reverse ETL BigQuery data to Spanner. This feature is generally available (GA).

The Gemini for Google Cloud API (cloudaicompanion.googleapis.com) is now enabled for existing BigQuery projects in the European jurisdiction.

You can now 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 in preview.

In BigQuery Data Transfer Service, you can monitor resource-level status reporting for Hive managed tables to track progress and view granular error details for individual tables. This feature is in preview.

You can use the BigQuery migration assessment for Snowflake to assess the complexity of migrating from Snowflake to BigQuery. This feature is generally available (GA).

You can now use the BigQuery Data Transfer Service remote MCP server to enable AI agents to create, manage, and run data transfers. This feature is in Preview.

The following functions are now generally available (GA):

• AI.EMBED: create embeddings from text or image data.
• AI.SIMILARITY: compute the semantic similarity between pairs of text, pairs of images, or across text and images.

You can clean, transform, and enrich data from files in Cloud Storage and Google Drive in your BigQuery data preparations. For more information, see Prepare data with Gemini. This feature is generally available (GA).

You can now use a custom organization policy to allow or deny specific operations on routines. This feature is in preview.

In BigQuery ML, you can now automatically deploy open models to Vertex AI endpoints. Automatically deployed models offer the following benefits:

• Automatic Vertex AI resource management
• Reserve open model resources by using Compute Engine reservations
• Automatic or immediate open model undeployment to save costs

This feature is generally available (GA).

BigQuery now lets you configure a global default location. This setting is used if the location isn't set or can't be inferred from the request. You can set the default location at the organization or project level.

This feature is generally available (GA).

BigQuery advanced runtime is now enabled as the default runtime for all projects.

You can now understand and debug BigQuery query performance with a visual mapping of your SQL query in the query execution graph. A heatmap highlights the steps that consume more slot time. This feature is generally available (GA).

Updates to conversational analytics include the following improvements:

• ObjectRef support: BigQuery conversational analytics now integrates with Google Cloud Storage through ObjectRef functions. This lets you reference and interact with unstructured data such as images and PDFs in Cloud Storage buckets in your conversational analysis.
• BQML support: BigQuery conversational analytics now supports a set of BigQuery ML functions, including AI.FORECAST, AI.DETECT_ANOMALIES, and AI.GENERATE. These functions let you perform advanced analytics tasks with simple conversational prompts.
• Chat with BigQuery results: You can now start conversations and chat with query results in BigQuery Studio (SQL editor).
• Enhanced support for partitioned tables: BigQuery conversational analytics can now use BigQuery table partitioning. The agent can optimize SQL queries by using partitioned columns such as date ranges on a date-partitioned table. This can improve query performance and reduce costs.
• Labels for agent-generated queries: BigQuery jobs initiated by the conversational analytics agent are now labeled in BigQuery Job History in the Google Cloud Console. You can identify, filter, and analyze the jobs run by the conversational analytics agent by referencing labels similar to {'ca-bq-job': 'true'}. These labels can help with the following tasks:
  • Monitor and attribute cost.
  • Audit agent activity.
  • Analyze agent-generated query performance.
• Suggest next questions (clickable): When working with BigQuery conversational analytics, the agent now suggests questions that are directly clickable in the Google Cloud console.

This feature is available in Preview.

You can create a remote model based on the Vertex AI gemini-embedding-001 model, or a remote model based on an open embedding model from Vertex Model Garden or Hugging Face that is deployed to Vertex AI.

You can then use the AI.GENERATE_EMBEDDING function with these remote models to generate embeddings. You can also use the AI.EMBED function directly with the gemini-embedding-001 model endpoint.

These features are generally available (GA).

You can now use the Pipelines & Connections page to streamline your data integration tasks by using guided, BigQuery-specific configuration workflows for services like BigQuery Data Transfer Service, Datastream, and Pub/Sub.

This feature is in Preview.

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

You can now use an alternate syntax when you call the VECTOR_SEARCH function to improve query performance when you search for a single vector. This feature is in Preview.

Monitor dataset replication latency and network egress bytes in Cloud Monitoring for BigQuery cross-region replication and managed disaster recovery. These metrics are generally available (GA).

You can now use continuous queries to stream BigQuery data to Spanner in real time. This feature is generally available (GA).

Effective June 1, 2026, BigQuery will limit legacy SQL use. This depends on whether your organization or project uses it from November 1, 2025, to June 1, 2026. If you don't use legacy SQL during this time, you won't be able to use it after June 1, 2026. If you do use it, your existing workloads will keep running, but new ones might not. For more information, see Legacy SQL feature availability.

You can now create and review custom glossary terms in BigQuery for a conversational analytics agent and you can review business glossary terms imported from Dataplex Universal Catalog for an agent. These terms help an agent interpret your prompts.

This feature is now in Preview.

You can now undelete a dataset that is within your time travel window to recover it to the state that it was in when it was deleted. This feature is generally available (GA).

You can now run global queries, which let you reference data stored in more than one region in a single query. This feature is in Preview.

After March 17, 2026, when you enable BigQuery, the BigQuery MCP server is automatically enabled.

Control of MCP use with organization policies is deprecated. After March 17, 2026, organization policies that use the gcp.managed.allowedMCPServices constraint won't work, and you can control MCP use with IAM deny policies. For more information about controlling MCP use, see Control MCP use with IAM deny policies.

The AI.CLASSIFY function now supports classifying your input into multiple categories. This feature is in Preview.

You can now provide descriptions for the fields in your custom output schema when you use the AI.GENERATE and AI.GENERATE_TABLE functions. This feature is generally available (GA).

You can now use dataset insights to understand relationships between tables in a dataset by generating relationship graphs and cross-table queries. You can automatically generate dataset summaries, infer relationships across tables, and receive suggestions for analytical questions. This feature is in Preview.

You can now run pipelines with three distinct execution methods: running all tasks, running selected tasks, and running tasks with selected tags. For more information, see Run a pipeline. This feature is generally available (GA).

You can now customize the scope of data documentation scans for BigQuery tables to generate specific insights. You can choose to generate only SQL queries, only table and column descriptions, or all insights.

You can also create one-time data scans that execute immediately upon creation, removing the need for a separate run command. These scans support a Time to Live (TTL) setting to automatically delete the scan resource after completion.

For more information, see Generate insights for a BigQuery table.

Data transfers from the YouTube Channel and YouTube Content Owner data sources now support reach reports. For more information, see YouTube Channel report transformation and YouTube Content Owner report transformation.

You can now associate data policies directly on columns. This feature enables direct database administration for controlling access and applying masking and transformation rules at the column level. This feature is now generally available (GA).

Gemini in BigQuery now processes data in the same jurisdiction (US or EU) as your BigQuery datasets, or based upon user-specified location settings. For more information, see Where Gemini BigQuery processes your data.

You can now pass parameterized queries from the BigQuery query editor in the Google Cloud console.

This feature is generally available (GA).

BigQuery now supports a RANDOM_HASH predefined masking rule. This rule returns a hash of the column's value using a salted hash algorithm, and it provides stronger security than the standard Hash (SHA-256) rule.

For more information, see Data masking rules.

BigQuery now offers conversational analytics, which accelerates data analysis by enabling insights through natural language. Users can view a predefined sample agent, chat with their BigQuery data or custom agents, and access those agents even outside of BigQuery. They can also use supported BigQuery ML functions in verified queries and in chat. This feature is in Preview.

You can now create BigQuery ML models by using the Google Cloud console.

This feature is generally available (GA).

The BigQuery change data capture feature has been renamed to BigQuery change data capture ingestion.

The BigQuery Data Transfer Service can now transfer data from Shopify to BigQuery. This feature is in Preview.

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

The BigQuery Data Transfer Service can now transfer data from Mailchimp to BigQuery. This feature is in Preview.

You can now use Gemini Cloud Assist to discover resources across your projects. For example, you can ask about a specific table's schema, or which tables contain demographic information about new users. This feature is in Preview.

You can now optionally specify which model to use by passing an endpoint argument to the AI.IF, AI.SCORE, and AI.CLASSIFY functions.

Support for table parameters in table-valued functions is restored.

You can now run queries that use the AI.IF, AI.SCORE, and AI.CLASSIFY functions by using your end-user credentials instead of a BigQuery connection.

BigQuery is now available in the Bangkok (asia-southeast3) region.

You can now use Gemini Cloud Assist to get information about your job history, such as why a particular query was slow or which queries were the most resource-intensive in the past day. This feature is in Preview.

Dataform workflows, BigQuery notebooks, pipelines, and data preparations are enforcing strict act-as mode at the project level. To avoid failures and maintain automatic releases, you must use custom service accounts instead of the default Dataform service agent across all repositories. You must also grant the Service Account User role (roles/iam.serviceAccountUser) to the default Dataform service agent and relevant principals. For more information and to verify act-as permissions, see Use strict act-as mode.

You can now use the Google-developed, open source Java Database Connectivity (JDBC) driver for BigQuery to connect your Java applications to BigQuery. This feature is in Preview.

The CREATE EXTERNAL TABLE and LOAD DATA statements now support the following options:

• time_zone: specify a time zone to use when loading data
• date_format, datetime_format, time_format, and timestamp_format: define how date and time values are formatted in your source files
• null_markers: define the strings that represent NULL values in CSV files.
• source_column_match: specify how loaded columns are matched to the schema. You can match columns by position or by name.

These features are generally available (GA).

The BigQuery Data Transfer Service can now transfer data from PostgreSQL to BigQuery. This feature is generally available (GA).