Data & Storage · Integration
Databricks
Add Databricks to your product for your customers, and give your AI agents governed access to it.
A Databricks integration inside your product opens a customer's lakehouse to your app without you standing up compute of your own. You can query tables registered in Unity Catalog, launch jobs, run notebooks and write results back, always against the workspace that customer authorised, with Unity Catalog's own grants deciding what is visible. The practical catch is cluster state: a query against a warm SQL warehouse returns quickly, while the same query against a cold cluster waits for it to spin up, so anything user-facing needs a plan for that first slow call. Workspace URLs and catalog layouts differ per customer as well. fastn keeps the tokens per tenant and absorbs API changes as they land.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Point your product at the workspace a customer authorised, so it reads tables through Unity Catalog rather than a copy.
Trigger a job or notebook run from an event in your product and collect the output once it finishes.
Write a result table back into the lakehouse so the customer's existing pipelines can consume it.
Design around cold cluster start so a user-facing query is either warmed in advance or openly asynchronous.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent queries a catalog table to answer a question, limited to the grants Unity Catalog gives its principal.
An agent starts an approved job run and reports the outcome, with the run attributed to the request.
An agent lists the catalogs and schemas it can reach before choosing where to look.
Example prompt
Query the gold sales table in this catalog for last month and show revenue by product.
Set up Databricks in 4 steps
- 01Enable the Databricks connector from your fastn dashboard.
- 02Have each customer authorise their own Databricks 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 Databricks integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Databricks integration without building it. Your customers connect their own Databricks 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 Databricks update is not your on-call problem.
- One integration serves your product and your agents. The same governed Databricks 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
Compare with
Snowflake
BigQueryWorks well with
dbt CloudOften used alongside
Tools the same teams tend to run next to Databricks, across other categories.
Anthropic Claude
OpenAI
Azure OpenAI
Hugging FaceDatabricks integration FAQ
How do I add a Databricks integration to my product?
Enable the Databricks connector in your fastn dashboard, then let each customer authenticate their own Databricks account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Databricks client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Databricks account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Databricks 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 Databricks 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 queries a catalog table to answer a question, limited to the grants Unity Catalog gives its principal.
Who maintains the Databricks integration?
fastn does. When Databricks 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 Databricks 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 Databricks 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 Databricks integration?
A common starting point: point your product at the workspace a customer authorised, so it reads tables through Unity Catalog rather than a copy. 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 Databricks integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Databricks does not change your per-connector cost. You can start free with 3 connected accounts.
Add Databricks to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.