Data & Storage · Integration
BigQuery
Add BigQuery to your product for your customers, and give your AI agents governed access to it.
With a BigQuery integration built into your product, a customer keeps analytics in their own project and your app queries it in place. Datasets, tables, views and query jobs are all addressed per customer, so results come back scoped to the project they authorised. The fact that dominates every BigQuery integration is the cost model: charges follow the bytes a query scans rather than the time it takes, which makes column selection and respect for partitioning and clustering the difference between a cheap feature and an expensive one. Job results are asynchronous too, so a long query is polled instead of waited on. fastn holds the service account credentials per tenant and follows Google's API changes.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let a customer authorise their own Google Cloud project so your product lists their datasets and queries their tables directly.
Load results your product produces into a customer's table so their existing dashboards pick them up with no export step.
Submit query jobs and collect results asynchronously, so a long-running analysis never blocks a request in your app.
Restrict reads to partitioned column ranges so the bytes a query scans, and therefore its cost, stay bounded.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent reads a customer's table to answer a question, and the dataset it touched is recorded on the call.
An agent inspects table schemas and partition columns before composing a query, so it does not scan a whole table by accident.
An agent writes an approved result table within the permissions its service account was granted.
Example prompt
Run a query over the events dataset for the last seven days and count conversions by source.
Set up BigQuery in 4 steps
- 01Enable the BigQuery connector from your fastn dashboard.
- 02Have each customer authorise their own BigQuery account, so calls run under their credentials rather than a shared key.
- 03Decide which records, datasets and fields your product needs, map those fields, then enable the actions and triggers you want.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the BigQuery integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a BigQuery integration without building it. Your customers connect their own BigQuery account inside your product and work their records, datasets and fields there, with no per-customer code on your side.
- Handle the part that actually costs time: schemas differ per customer and change without notice, and volumes can be large. fastn owns the auth, token refresh, rate limits, pagination and breaking-change fixes, so a BigQuery update is not your on-call problem.
- One integration serves your product and your agents. The same governed BigQuery connection powers in-product features and gives AI agents scoped, audited access, so you read and write your customers' data where it already lives without wiring it twice.
Used by these teams
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Hugging FaceBigQuery integration FAQ
How do I add a BigQuery integration to my product?
Enable the BigQuery connector in your fastn dashboard, then let each customer authenticate their own BigQuery account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no BigQuery client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own BigQuery account?
Yes. Every connection is scoped to the individual customer, so each authorises their own BigQuery account and only ever sees their own records, datasets and fields. That per-tenant isolation is the point of an embedded integration: you support the long tail of customer setups without maintaining an integration per customer.
Can AI agents use this BigQuery integration?
Yes. The same connection is exposed to your agents through the fastn MCP gateway, with permissions scoped per tenant and every call audited. An agent reads a customer's table to answer a question, and the dataset it touched is recorded on the call.
Who maintains the BigQuery integration?
fastn does. When BigQuery changes an endpoint, deprecates a field or alters its auth, the fix lands in the connector rather than in your backlog, and your customers' connections keep working.
Does the BigQuery integration adapt when a customer's schema changes?
Schema and field mapping is configuration per customer, so a change on their side is a mapping update rather than a code change and a release on yours.
How are large BigQuery reads handled?
Pagination and throttling are handled for you, and initial backfills are rate-limited so a large import does not exhaust a customer's API allowance.
What can I build with the BigQuery integration?
A common starting point: let a customer authorise their own Google Cloud project so your product lists their datasets and queries their tables directly. Teams also use it for the other use cases listed above, and expose it to agents for governed reads and writes.
How much does the BigQuery integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding BigQuery does not change your per-connector cost. You can start free with 3 connected accounts.
Add BigQuery to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.