Data & Storage · Integration
Azure Data Explorer
Add Azure Data Explorer to your product for your customers, and give your AI agents governed access to it.
Azure Data Explorer is built for append-heavy telemetry, and reading it means writing KQL, a language of its own where a result is shaped by a pipeline of operators rather than by SQL clauses. What surprises people is ingestion latency. The default path batches rows behind the scenes before they become queryable, so data your product just sent is not immediately in a result, and closing that gap means enabling streaming ingestion on a table and living with different limits for it. Cluster and database are addressed separately from the query, and a table's retention and caching policy decides how far back a question can even reach. Per-customer credentials sit with fastn, which keeps the query and ingestion endpoints current.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Point your product at the cluster a customer already runs, so their databases are queried in place and the rows never leave.
Run KQL from your product and render the result directly, instead of staging rows in a warehouse first.
Choose batched or streaming ingestion per table, so what your product promises about freshness matches the path it sends on.
Read table schemas and retention policies during onboarding so your product only offers time ranges the data covers.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent writes a KQL query against the tables a customer exposed and explains what came back.
An agent checks a table's retention window before it answers a question about last quarter.
An agent reports how recent the newest row in a table is, so nobody trusts a stale panel.
Example prompt
Query this table for errors in the last hour and tell me how fresh the newest row is.
Set up Azure Data Explorer in 4 steps
- 01Enable the Azure Data Explorer connector from your fastn dashboard.
- 02Have each customer authorise their own Azure Data Explorer account, so calls run under their credentials rather than a shared key.
- 03Decide which databases, tables and KQL queries your product needs, and note that batched ingestion means just-sent rows are not queryable yet.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the Azure Data Explorer integration
What you get by embedding it with fastn instead of building it yourself.
- Ship an Azure Data Explorer integration without building it. Your customers connect their own Azure Data Explorer 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 an Azure Data Explorer update is not your on-call problem.
- One integration serves your product and your agents. The same governed Azure Data Explorer 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.
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How do I add an Azure Data Explorer integration to my product?
Enable the Azure Data Explorer connector in your fastn dashboard, then let each customer authenticate their own Azure Data Explorer account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Azure Data Explorer client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Azure Data Explorer account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Azure Data Explorer 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 Azure Data Explorer 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 writes a KQL query against the tables a customer exposed and explains what came back.
Who maintains the Azure Data Explorer integration?
fastn does. When Azure Data Explorer 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 Azure Data Explorer 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 Azure Data Explorer 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 Azure Data Explorer integration?
A common starting point: point your product at the cluster a customer already runs, so their databases are queried in place and the rows never leave. 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 Azure Data Explorer integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Azure Data Explorer does not change your per-connector cost. You can start free with 3 connected accounts.
Add Azure Data Explorer to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.