BigQuery release notes

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.

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.

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

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).