BigQuery API

Name: BigQuery API API
Brand: BigQuery API
Availability: InStock

✓ Official Vendor SpecAnalyticsBusiness Intelligenceoauth242 EndpointsREST

For Agents

Run SQL queries, manage datasets and tables, and orchestrate load/extract jobs in Google BigQuery. Agents can submit query jobs, page through results, and stream rows into tables.

Quickstart

Get started with BigQuery API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"run a SQL query on BigQuery"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with BigQuery API API.

Submit asynchronous query jobs and poll their results through the jobs resource

Create and update datasets, tables, and views with custom schemas and partitioning

Stream rows into tables with insertAll for near-real-time analytics ingestion

List and copy tables across datasets and projects for data sharing workflows

GET STARTED

Start building with BigQuery API API

Explore with Jentic

View OpenAPI Document

BigQuery is Google Cloud's serverless data warehouse for running SQL analytics over petabyte-scale datasets without managing infrastructure. The API exposes datasets, tables, jobs, models, routines, and row access policies so you can load data, run queries, stream inserts, schedule extracts, and manage ML routines from code. It supports federated queries across Cloud Storage, Bigtable, and external sources, and integrates with Google's IAM for fine-grained access control. Pricing is consumption-based on bytes scanned plus storage, with flat-rate reservations available for predictable workloads.

Use Cases

Patterns agents use BigQuery API API for, with concrete tasks.

★ Ad-hoc Analytical Querying

Submit SQL queries against terabyte-to-petabyte datasets and stream paginated results back to an application. Jobs are submitted to /projects/{projectId}/queries or as insert jobs and resolved via /projects/{projectId}/jobs/{jobId}, with results pulled from /projects/{projectId}/queries/{jobId}/getQueryResults. Typical interactive queries return in seconds; long-running jobs can be polled or cancelled.

Submit a query job that selects the top 10 product_ids by sum(revenue) from project.dataset.orders for the last 7 days, poll until done, and return the rows.

Pipeline Ingestion and Loading

Load batch files from Cloud Storage into managed tables and stream live events with insertAll. Load jobs are created at /projects/{projectId}/jobs with configuration.load, while streaming uses /projects/{projectId}/datasets/{datasetId}/tables/{tableId}/insertAll. This supports ELT pipelines that land raw data, then transform it with scheduled query jobs.

Create a load job that ingests gs://bucket/events/2026-06-09/*.json into project.analytics.events with schema autodetect and WRITE_APPEND disposition.

Dataset and Schema Governance

Create datasets per environment, apply IAM-based access controls, manage table schemas, and set retention and partitioning. Datasets are managed at /projects/{projectId}/datasets and tables at /projects/{projectId}/datasets/{datasetId}/tables, with patch operations for schema evolution. Combined with row access policies and labels, teams can enforce data governance at the warehouse layer.

Create dataset analytics_prod in US multi-region, then create a date-partitioned table events with the supplied JSON schema.

BigQuery ML Model Management

Train, list, and delete BigQuery ML models and routines that run inference inside the warehouse. Models live under /projects/{projectId}/datasets/{datasetId}/models and routines under /projects/{projectId}/datasets/{datasetId}/routines. Useful for forecasting, classification, and embedding-generation directly on warehouse data without exporting it.

List all models in dataset analytics_prod and delete any model whose creation_time is older than 30 days.

AI Agent Analytics Assistant

Connect an AI agent to BigQuery so business users can ask plain-English questions and receive validated results. The agent uses Jentic to discover the BigQuery query operation, generate SQL, submit a job, poll for completion, and format the response. Credentials stay in the Jentic vault, so the agent never sees raw OAuth tokens.

Translate the user prompt 'how many signups did we get last week by country' into a BigQuery SQL job, run it, and return a table of country and count.

Key Endpoints

42 endpoints — bigquery is google cloud's serverless data warehouse for running sql analytics over petabyte-scale datasets without managing infrastructure.

METHOD

PATH

DESCRIPTION

POST

/projects/{+projectId}/queries

Run a SQL query and return results synchronously where possible

POST

/projects/{+projectId}/jobs

Submit a query, load, copy, or extract job

GET

/projects/{+projectId}/jobs/{+jobId}

Get the status and statistics of a job

POST

/projects/{+projectId}/datasets

Create a new dataset

POST

/projects/{+projectId}/datasets/{+datasetId}/tables

Create a new table in a dataset

POST

/projects/{+projectId}/datasets/{+datasetId}/tables/{+tableId}/insertAll

Stream rows into a table

GET

/projects/{+projectId}/datasets/{+datasetId}/tables/{+tableId}

Get a table's schema and metadata

POST

/projects/{+projectId}/queries

Run a SQL query and return results synchronously where possible

POST

/projects/{+projectId}/jobs

Submit a query, load, copy, or extract job

GET

/projects/{+projectId}/jobs/{+jobId}

Get the status and statistics of a job

POST

/projects/{+projectId}/datasets

Create a new dataset

POST

/projects/{+projectId}/datasets/{+datasetId}/tables

Create a new table in a dataset

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Google OAuth 2.0 client credentials and refresh tokens for BigQuery are stored encrypted in the Jentic vault (MAXsystem). Agents receive short-lived scoped access tokens at execution time, never the underlying client secret or refresh token.

Intent-based discovery

Agents search Jentic by intent (e.g., 'run a BigQuery query' or 'stream rows into BigQuery') and Jentic returns the matching operation with its input schema, so the agent can call the right endpoint without browsing the Discovery doc.

Time to first call

Direct BigQuery integration: 1-3 days to set up OAuth, scopes, and job polling. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

BigQuery Data Transfer API

Schedules recurring loads from SaaS sources into BigQuery tables.

Choose this when the agent needs to schedule a recurring transfer rather than submit a one-off query or load job.

Complementary

BigQuery Reservation API

Manages BigQuery slot reservations and capacity commitments.

Choose this when the agent needs to allocate flat-rate slots or assign reservations to projects, not run queries.

Alternative

Snowflake API

Cloud data warehouse with similar SQL analytics scope on a different vendor.

Choose Snowflake when the data already lives there or when the team standardises on multi-cloud warehousing.

FAQs

Specific to using BigQuery API API through Jentic.

What authentication does the BigQuery API use?

The BigQuery API uses Google OAuth 2.0 with scopes such as https://www.googleapis.com/auth/bigquery and https://www.googleapis.com/auth/cloud-platform. Jentic stores the OAuth client and refresh token in its encrypted vault and hands the agent a scoped access token at call time, so raw client secrets never enter agent context.

Can I run SQL queries with the BigQuery API?

Yes. Submit a query job via POST /projects/{projectId}/queries for synchronous calls or POST /projects/{projectId}/jobs with a query configuration for long-running jobs, then poll GET /projects/{projectId}/jobs/{jobId} until status.state is DONE and read rows from getQueryResults.

What are the rate limits for the BigQuery API?

BigQuery enforces project-level quotas rather than a per-second rate cap: typical limits include 100 concurrent interactive queries per project and 1,500 table operations per table per day. Streaming insertAll is capped at 1 GB per second per project. Check the Google Cloud quotas console for current values for your project.

How do I stream rows into a BigQuery table through Jentic?

Search Jentic with 'stream rows into a BigQuery table', load the schema for POST /projects/{projectId}/datasets/{datasetId}/tables/{tableId}/insertAll, and execute with the rows array. Jentic injects the OAuth token, so the agent only supplies projectId, datasetId, tableId, and the row payload.

Is the BigQuery API free?

BigQuery offers a free tier of 1 TB of query data processed and 10 GB of storage per month; beyond that, you pay per byte scanned (on-demand) or via flat-rate slot reservations. Streaming inserts and most API operations are also metered. The API itself has no per-call surcharge.

Can I cancel a long-running BigQuery query?

Yes, call POST /projects/{projectId}/jobs/{jobId}/cancel. The job moves to a cancelling state and any partial results are discarded; you are not billed for bytes scanned after cancellation propagates.