# Google BigQuery AI integration on Definable > Google BigQuery is a fully managed data warehouse for large-scale data analytics, offering fast SQL queries and machine learning capabilities on massive datasets ## What this connects Google BigQuery is a fully managed data warehouse for large-scale data analytics, offering fast SQL queries and machine learning capabilities on massive datasets Vendor: https://cloud.google.com/bigquery ## Tools available **63** tools available. First 12: - `GOOGLEBIGQUERY_CANCEL_JOB` — Cancel BigQuery Job — Tool to cancel a running BigQuery job. This call returns immediately, and you need to poll for the job status to see if the cancel completed successfully. Note that cancelled jobs may still incur costs. - `GOOGLEBIGQUERY_CREATE_CAPACITY_COMMITMENT` — Create Capacity Commitment — Tool to create a new capacity commitment resource in BigQuery Reservation. Use when you need to purchase compute capacity (slots) with a committed period of usage for BigQuery jobs. Supports various commitment plans (FLEX, MONTHLY, ANNUAL, THREE_YEAR) and editions (STANDARD, ENTERPRISE, ENTERPRISE_PLUS). - `GOOGLEBIGQUERY_CREATE_CONNECTION` — Create BigQuery Connection — Tool to create a new BigQuery connection to external data sources using the BigQuery Connection API. Use when setting up connections to AWS, Azure, Cloud Spanner, Cloud SQL, Salesforce DataCloud, or Apache Spark. - `GOOGLEBIGQUERY_CREATE_DATA_EXCHANGE` — Create Analytics Hub Data Exchange — Tool to create a new Analytics Hub data exchange for sharing BigQuery datasets. Use when you need to set up a container for data sharing with descriptive information and listings. - `GOOGLEBIGQUERY_CREATE_DATAEXCHANGES_LISTINGS` — Create Analytics Hub Listing — Tool to create a new listing in a BigQuery Analytics Hub data exchange. Use when you need to share a BigQuery dataset with specific subscribers or make it available for discovery. The dataset must exist and be in the same region as the data exchange. - `GOOGLEBIGQUERY_CREATE_DATASET` — Create BigQuery Dataset — Tool to create a new BigQuery dataset with explicit location, labels, and description using the BigQuery Datasets API. Use when the workflow needs to set up a staging/warehouse dataset and correctness of region is critical to avoid downstream job location mismatches. Surfaces 409 Already Exists errors cleanly without retrying. - `GOOGLEBIGQUERY_CREATE_LISTING` — Create Analytics Hub Listing — Tool to create a new listing in a data exchange using Analytics Hub API. Use when publishing a BigQuery dataset to make it available for subscription by other users or organizations. - `GOOGLEBIGQUERY_CREATE_LOCATIONS_DATAPOLICIES` — Create BigQuery Data Policy (v2beta1) — Tool to create a new data policy under a project with specified location using the v2beta1 BigQuery Data Policy API. Use when you need to set up data masking rules or column-level security for sensitive data. The v2beta1 endpoint uses a nested request structure. - `GOOGLEBIGQUERY_CREATE_QUERY_TEMPLATE` — Create Analytics Hub Query Template — Tool to create a new query template in a BigQuery Analytics Hub Data Clean Room (DCR) data exchange. Use when you need to define predefined and approved queries for data clean room use cases. Query templates must be created in DCR data exchanges only. - `GOOGLEBIGQUERY_CREATE_RESERVATION` — Create BigQuery Reservation — Tool to create a new BigQuery reservation resource to guarantee compute capacity (slots) for query and pipeline jobs. Use when you need to reserve dedicated compute resources for predictable performance and cost management. Reservations can be configured with autoscaling, concurrency limits, and edition-based features. - `GOOGLEBIGQUERY_CREATE_RESERVATION_ASSIGNMENT` — Create BigQuery Reservation Assignment — Tool to create a BigQuery reservation assignment that allows a project, folder, or organization to submit jobs using slots from a specified reservation. Use when setting up resource allocation for BigQuery workloads. Note: A resource can only have one assignment per (job_type, location) combination. - `GOOGLEBIGQUERY_CREATE_ROUTINE` — Create BigQuery Routine — Tool to create a new user-defined routine (function or procedure) in a BigQuery dataset. Use when you need to define SQL, JavaScript, Python, Java, or Scala functions/procedures for reusable logic, data transformations, or custom masking. Supports scalar functions, table-valued functions, procedures, and aggregate functions with comprehensive type definitions. ## Auth Auth schemes: `OAUTH2`, `GOOGLE_SERVICE_ACCOUNT`. Managed by Definable: `OAUTH2` — no client credentials required from the user. ## How agents use Google BigQuery Inside a Definable workflow, Google BigQuery is one of the tools the **Distributor specialist** can call. Example coordination patterns: - **Researcher → Google BigQuery** — the Researcher (GPT-5.5) pulls context from Google BigQuery (records, threads, documents), synthesises findings, and briefs the rest of the team. - **Writer → Distributor → Google BigQuery** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Google BigQuery (create record, post message, draft email). - **Designer / Engineer → Distributor → Google BigQuery** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Google BigQuery (attach file, open PR comment, post status). The Verifier checks every Google BigQuery call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention. ## Categories - databases — https://definable.ai/apps/category/databases/ - analytics — https://definable.ai/apps/category/analytics/ ## Related - HTML page: https://definable.ai/apps/googlebigquery/ - Same category (databases): https://definable.ai/apps/category/databases/ - All integrations: https://definable.ai/apps/ - Workflow (multi-agent loop): https://definable.ai/workflow/ - Apps llms.txt index: https://definable.ai/llms-apps.txt