Starting July 25, 2026, the BigQuery Data Transfer Service for Facebook Ads

## Change Change Starting July 25, 2026, the [BigQuery Data Transfer Service for Facebook Ads connector](https://cloud.google.com/bigquery/docs/facebook-ads-transfer) will update the data type mapping for the `ActionValue` field in the `AdInsightsActions` report from `INT`to `FLOAT`. ## Feature Feature The following features have been added to [Python UDFs](https://cloud.google.com/bigquery/docs/user-defined-functions-python)during [Preview](https://cloud.google.com/products/#product-launch-stages): * Vectorized UDFs with Apache Arrow. You can now create [vectorized Python UDFs](https://cloud.google.com/bigquery/docs/user-defined-functions-python#create-vector-udf-apache)using the Apache Arrow `RecordBatch` interface for improved performance. * Cloud Monitoring integration. Python UDFs now export[metrics](https://cloud.google.com/bigquery/docs/user-defined-functions-python#view%5Fpython%5Fudf%5Fmetrics)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](https://cloud.google.com/bigquery/quotas#udf%5Flimits)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](https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#externalservicecost). ## Feature Feature You can now [migrate metadata from external data catalogs to BigLake tables for Apache Iceberg](https://cloud.google.com/bigquery/docs/migration/external-metastore-lakehouse-migration). This feature supports external data catalogs such as such as Apache Hive Metastore and Apache Iceberg REST Catalog. This feature is in[Preview](https://cloud.google.com/products#product-launch-stages). ## Feature Feature You can use the [BigQuery MCP server](https://cloud.google.com/bigquery/docs/use-bigquery-mcp)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](https://cloud.google.com/products#product-launch-stages)(GA). ## Feature Feature You can now publish a [BigQuery Conversational Analytics agent in Gemini Enterprise](https://cloud.google.com/bigquery/docs/create-data-agents#publish-agent-gemini-enterprise). This feature is in[Preview](https://cloud.google.com/products/#product-launch-stages). ## Feature Feature You can now use the [notebook gallery](https://cloud.google.com/bigquery/docs/notebooks-introduction#notebook%5Fgallery)in the BigQuery web UI as your central hub for discovering and using prebuilt notebook templates. This feature is [generally available](https://cloud.google.com/products/#product-launch-stages)(GA).