Project caps (also known as scheduling policies) let you limit maximum slots and concurrency per project within a BigQuery reservation. This feature is only supported in the BigQuery Enterprise and Enterprise Plus editions and is generally available (GA).

Feature

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. This feature is only supported in the BigQuery Enterprise and Enterprise Plus editions and is generally available (GA).

Feature

You can use the JSON_FLATTEN function to extract all non-array values that are either directly in the input JSON value or children of one or more consecutively nested arrays in the input JSON value. This function is generally available. (GA).

You can use graph measures to lock an aggregation to a key in order to perform complex aggregations without overcounting. Measure expressions can include the following:

• Aggregate function calls
• Expressions within aggregate functions
• Conditional logic within aggregate functions
• Conditional distinct counting
• Scalar operations and expressions outside of aggregate functions
• Combinations of measures wrapped in the AGG function

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. The GRAPH_EXPAND function now supports multiple fact tables. To view the output schema of the GRAPH_EXPAND function, use the BQ.SHOW_GRAPH_EXPAND_SCHEMA system procedure.

You can define extensions in the OPTIONS clause of the labels and properties in your graphs, which are string pairs that represent custom metadata for natural language querying.

Measures and graphs are supported in Data Studio through the BigQuery connector. Measures and graphs are also automatically cataloged in Knowledge Catalog, which makes them searchable by natural language or domain synonyms.

These features are generally available (GA).

Feature

BigQuery generative AI functions now support the gemini-4-carbon model. This model is generally available.

To support your FinOps practices, you can now opt in to compute cost attribution in BigQuery editions. This feature attributes billed compute charges directly to the query projects that use your administration project's reservation capacity. Allocating costs based on actual commitment and autoscaling slot usage lets you accurately track project-level costs and manage internal chargebacks. This feature is available in Preview.

Feature

Query performance troubleshooting and job and system performance comparison in the BigQuery jobs explorer are generally available (GA). For more information, see Troubleshoot job performance and Diagnose performance regressions by comparing jobs and systems.

Feature

When you create or schedule a pipeline, you can specify the maximum number of concurrently executed pipeline tasks. This feature is generally available (GA).

Feature

You can use compression functions in GoogleSQL to compress or decompress bytes or string values by using the Zstandard (Zstd) lossless data compression algorithm. The following functions are supported:

• ZSTD_COMPRESS
• ZSTD_DECOMPRESS_TO_BYTES
• ZSTD_DECOMPRESS_TO_STRING

These functions are in Preview.

You can now use BigQuery Graph and run Graph Query Language (GQL) queries when you use on-demand pricing.

Breaking

Starting January 31, 2027, the BigQuery Data Transfer Service for Shopify connector will no longer support the following fields:

• The legacyMode field from the DeliveryProfile dataset schema
• The permitSkuSharing field from the FulfillmentService dataset schema

For more information, see January 31, 2027.

Feature

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

In the query execution graph's query text heatmap, you can now view and navigate through a hierarchical list of context references, such as nested function calls or view references, for stage steps. This feature is generally available (GA).

Feature

In the query execution graph, the query text heatmap now supports displaying the SQL text for persistent and temporary user-defined functions (UDFs). This feature is generally available (GA).

Change

The BigQuery Metadata curation console page has been removed. You can now use Knowledge Catalog to schedule Cloud Storage discovery scans, manage data profiling and data quality, and perform related data governance tasks.

Feature

Conversational analytics in BigQuery is now available in the asia-northeast1 (Tokyo) location. For more information, see Locations.

You can now use persistent and temporary SQL user-defined functions (UDFs) in BigQuery continuous queries to encapsulate and reuse custom real-time transformation logic. Only SQL UDFs are supported in continuous queries.

Change

In BigQuery Studio, the left pane has been updated with new tab names. The Classic Explorer tab has been renamed to the Data tab, and the Repository tab has been renamed to the Source control tab. The Data tab now includes the Add data button and focuses on data hierarchies. These changes are generally available (GA).

Lakehouse caching for cross-cloud data access is generally available (GA). Cross-cloud caching is disabled by default. You can opt in to caching on a Lakehouse federated catalog or a BigQuery cross-cloud connection to cache remote files in your Google Cloud region. This helps reduce cross-cloud egress costs and accelerate subsequent queries.

For more information, see About cross-cloud data access and Query remote data.

Feature

Lakehouse supports the following remote catalog providers for cross-cloud data access. This feature is generally available (GA).

• Databricks Unity Catalog on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud
• AWS Glue Data Catalog on AWS
• Snowflake Horizon Catalog on AWS, Microsoft Azure, or Google Cloud
• Workday Data Lake on AWS or Google Cloud

This feature lets you configure federated catalog endpoints to synchronize catalog metadata and query Apache Iceberg tables in remote catalogs directly from Google Cloud using BigQuery, Managed Service for Apache Spark, and Apache Spark without migrating data or building complex ETL pipelines. You can route background metadata refresh and queries over the public internet or privately over Cross-Cloud Interconnect, and protect cross-cloud data access with VPC Service Controls service perimeters. For Databricks Unity Catalog, you can also use user identity federation to propagate end-user identities on queries for permission enforcement and audit logging.

For more information, see About cross-cloud data access, Query remote data, and Use VPC Service Controls with federated catalog endpoints.

Feature

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 generally available (GA).

Feature

You can use the AI.PARSE_DOCUMENT function to extract layout elements such as titles, headings, paragraphs, lists, tables, and images by using the Document AI layout parser to convert unstructured documents into structured JSON data.

This function is generally available (GA).

Feature

You can now use BigQuery change history and continuous queries for Apache Iceberg managed tables in Lakehouse.

You can use the APPENDS table-valued function (TVF) to track rows appended to Apache Iceberg managed tables over a specified time range, and use BigQuery continuous queries to read incoming data from and write output rows to Apache Iceberg managed tables.

This feature is generally available (GA).

For more information, see Work with change history and Introduction to continuous queries.

Feature

The SECURE_CONTEXT function is now available in Preview. SECURE_CONTEXT returns the value of a request-scoped secure context parameter provided at query runtime, allowing logical views to dynamically filter and evaluate data based on context variables such as user attributes, department IDs, or tenant identifiers.

Feature

The Data Engineering Agent now supports BigQuery AI, ML, and object functions and statements in generated Dataform SQLX pipelines and agent instruction files.

This feature is generally available (GA).

You can now retrieve query results directly in the Apache Arrow format for faster retrieval speed and improved serialization efficiency. This feature is in Preview.

The Data Engineering Agent now integrates with Google Drive, letting you provide Google Sheets files, CSV, or JSONL files as input for your data engineering pipelines.

This feature is generally available (GA).

Feature

You can now use conversational analytics in Connected Sheets to ask questions and analyze your BigQuery data using natural language. The Gemini side panel can help you translate your questions into optimized SQL queries and execute them, summarize key trends, and insert live, refreshable database objects directly into your workbook.

This feature is generally available (GA).

Feature

The AI.COUNT_TOKENS function, which estimates the token count of a text string, is generally available (GA).

Conversation logging for Conversational analytics in BigQuery is now generally available (GA). You can log data agent conversations to a BigQuery table in the agent's project. For more information, see Conversation logging overview and Configure settings.

Feature

Python user-defined functions now support volume mounts, which let you access dependency files from Cloud Storage directly from your Python UDF code. This feature is in Preview.

Feature

The REGEXP_EXTRACT_GROUPS function produces substrings that match multiple capturing groups in a regular expression.

This function is in Preview.

The AI.TRANSLATE function lets you translate text strings between languages. AI.TRANSLATE supports standard Neural Machine Translation (NMT) workloads and Translation LLM (TLLM) capabilities.

This function is generally available (GA).

The AI.CHUNK_DOCUMENT function is a table-valued function that splits unstructured documents into semantically coherent text chunks optimized for vector embeddings and Retrieval-Augmented Generation (RAG) workflows.

This function is generally available (GA).

Conversational analytics in BigQuery is now available in the following locations:

• northamerica-northeast1 (Montréal)
• northamerica-northeast2 (Toronto)

For more information, see Locations.

You can copy tables across regions. This feature is now generally available (GA).

Feature

You can now use reusable column aliases in GoogleSQL. This feature lets you define an alias for an expression in the SELECT clause of a SQL query and immediately reference that alias within the same SELECT list or in the WHERE clause of the same query block.

Reusable column aliases also streamline the migration of workloads from other data warehouses such as Snowflake and Teradata, by supporting their syntax.

This feature is generally available, and is opt-in only. To use reusable column aliases, you must first enable the feature.

Feature

You can export a conversational analytics agent's answer, including text, visualizations, tables, and citations, to a PDF or CSV file. This feature is generally available (GA).

The Apache Hive catalog endpoint in the Lakehouse runtime catalog is now generally available (GA). This integration lets you use the metastore as a serverless, central metadata layer, enabling seamless data sharing and unified querying across Apache Spark, Apache Hive, and BigQuery.

For more information, see About the Hive catalog endpoint and Set up Spark and Hive with the Lakehouse runtime catalog.

Feature

Metadata migration from Dataproc Metastore to Lakehouse is now generally available (GA). You can perform a one-time backfill migration or dry run to migrate metadata from a Dataproc Metastore service to an Apache Iceberg REST catalog endpoint or a Hive catalog endpoint using the Google Cloud console, the gcloud CLI, or the REST API.

For more information, see Migrate metadata from Dataproc Metastore to Lakehouse.

Feature

Conversational analytics now lets you create a conversation with all of the data sources in multiple projects, or create a conversation with a dataset. These features are generally available (GA).

Feature

You can publish a BigQuery Conversational Analytics agent in Gemini Enterprise. This feature is now generally available (GA).

Feature

The AI.AGG function now supports the output_schema argument, which lets you specify the schema of the function output as a comma-separated list of fields. This feature is generally available (GA).

The AI.AGG function is generally available (GA). You can use this function to semantically aggregate unstructured input data based on natural language instructions.

Feature

BigQuery generative AI functions now support the gemini-4-argon model. This model is generally available.

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

The BigQuery Data Engineering Agent is now available in the asia-northeast1 (Tokyo) regional endpoint and supports the gemini-3.5-flash model. For more information, see Where Gemini in BigQuery processes your data.

This feature is Generally Available (GA).

Feature

Lakehouse now supports change data capture (CDC) ingestion from Delta Lake and Apache Iceberg tables (Preview).

You can now synchronize CDC transaction logs (inserts, updates, deletes) from Delta Lake and Apache Iceberg tables into Lakehouse Apache Iceberg tables using Dataflow batch jobs (for Delta Lake) or batch and streaming jobs (for Apache Iceberg). For more information, see Migrate Delta Lake CDC data into Lakehouse using Dataflow and Migrate Apache Iceberg CDC data into Lakehouse using Dataflow.

BigQuery offers TimesFM 3.0, a built-in time series forecasting model that implements Google Research's open source TimesFM model. You can use TimesFM 3.0 with the following functions:

• Use AI.FORECAST to perform time series forecasting. In addition to univariate forecasting, you can now perform multivariate forecasting that predicts values for multiple time series based on historical values and additional covariates.
• Use AI.EVALUATE to perform univariate time series evaluation.
• Use AI.DETECT_ANOMALIES to perform univariate time series anomaly detection.

Conversational analytics in BigQuery also supports using TimesFM 3.0 with these functions.

To try using the TimesFM 3.0 model for multivariate forecasting with the AI.FORECAST function, see Forecast a single time series with a TimesFM multivariate model and Forecast multiple time series with a TimesFM multivariate model.

The TimesFM 3.0 model is in Preview.

Conversational analytics in BigQuery now supports the AI.CAUSAL_EFFECT function to quantify the impact of specific interventions on time series data. This feature is in Preview.

Iceberg external tables and Iceberg managed tables now support flexible column names by default. This feature is generally available (GA).

Feature

The gemini-embedding-2 model works well for embedding long strings, including multilingual and unstructured data. It supports a mix of text, images, audio, video, and PDF files. You can use this model in the AI.EMBED, AI.SIMILARITY, and AI.GENERATE_EMBEDDING functions. This model is generally available.

The Rust SDK for BigQuery is now generally available (GA).

Feature

The Data Engineering Agent now supports the gemini-3.7-flash model for the us, eu, and global multi-regional endpoints.

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

Feature

Chatting with graphs in conversational analytics is now generally available. You can use a combination of multiple graphs, tables, views, and UDFs as data sources.

Feature

BigQuery pipelines provides support for automated metadata enrichment and Knowledge Catalog data quality scorecard integration. In addition, the Data Engineering Agent can proactively generate semantic metadata for your pipeline assets. For more information, see Metadata enrichment and data quality scorecard integration. These features are generally available.

The BigQuery Security center is now available in the Google Cloud console. You can use the Security center to analyze your data security profile, create and manage row- and column-level security policies, and configure and manage data governance and policy tags. This feature is generally available (GA).

Feature

BigQuery jobs explorer layout and filtering improvements, including status counts in the filter bar, resource grouping, and timeline metric charts, are generally available (GA).

Feature

You can now use the OBJ.LIST function to perform spontaneous discovery and analysis of unstructured data. The OBJ.LIST function returns a table of metadata and ObjectRef values for files stored in Cloud Storage, and does not require you to create a persistent object table.

This feature is generally available (GA).

Feature

You can now view the UPDATE, DELETE, MERGE, and EXPORT execution steps in your query plans.

The AI.KEY_DRIVERS function is now generally available (GA). You can use the AI.KEY_DRIVERS function to identify segments of data that cause statistically significant changes to a summable metric.

Feature

You can query the INFORMATION_SCHEMA.FAILOVER_HISTORY view to retrieve a near real-time list of failover events for reservations within an administration project that use managed disaster recovery.

This feature is now in (Preview).

The BigQuery Data Transfer Service MCP server is now Generally Available (GA).

Feature

You can now write the output rows produced by BigQuery continuous queries directly into Apache Iceberg managed tables. This lets you continuously process streaming data from BigQuery and write it into your open-format lakehouse by using an INSERT DML statement.

In the BigQuery Studio query editor, the Query results table now keeps the row numbers visible on the left as you scroll horizontally. This helps you maintain context and track data across wide tables. This feature is generally available (GA).

You can now publish a BigQuery data agent in Gemini Enterprise by registering the agent with Agent Registry and importing it using default Google-managed credentials. When BigQuery and Gemini Enterprise are in the same Google Cloud project and configured with a matching Agent Gateway region, you don't need to manually copy the Agent-to-Agent (A2A) JSON card or configure OAuth client credentials.

These features are in Preview.

Lakehouse now supports Workday Data Lake as a remote catalog provider for cross-cloud data access (Preview). You can configure federated catalogs to query data stored in Workday Data Lake 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 connection for Workday Data Lake.

Feature

The Query results pane in the BigQuery Studio query editor lets you view a short history of recent runs for a query, including multi-statement queries, without having to navigate to the Job history tab. This feature is generally available (GA).

Feature

BigQuery generative AI functions now support the gemini-3.8-flash Gemini model.

You can add unit tests to pipelines to validate your SQL transformation logic against mock datasets. Unit tests for pipelines are generally available (GA).