Google BigQuery API
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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.
Use for: I need to run a SQL query against a BigQuery dataset, List all tables in a specific BigQuery dataset, Get the schema of a BigQuery table before inserting rows, Stream new event rows into a BigQuery table for real-time analytics
Not supported: Does not handle Cloud Storage object management, Pub/Sub messaging, or Dataflow pipeline execution — use for warehouse query, load, and metadata operations only.
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.
Install Jentic One Beta
Connect the BigQuery API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the BigQuery API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with BigQuery 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
Manage routines and BigQuery ML models for in-warehouse training and inference
Apply row access policies and IAM bindings to enforce column- and row-level security
Cancel running jobs and inspect job statistics for cost and performance tuning
Use Cases
Patterns agents use BigQuery 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
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
Why 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.
FAQs
Specific to using BigQuery 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.
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