Cursor + Bruin

Source

Ingest Cursor data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Engineering metrics in business terms
Join Cursor data with revenue and customer data. Show leadership how engineering reliability impacts the bottom line.
DORA metrics, automated
Cursor data feeds deployment frequency, lead time, MTTR, and change failure rate calculations automatically.
Catch data gaps
Quality checks ensure Cursor data is complete and fresh. Stale metrics mean bad decisions, Bruin catches it.
Cross-tool visibility
Combine Cursor with Jira, GitHub, PagerDuty, and other tools. See the full engineering picture in one place.

For data & engineering teams

How it works

Freshness checks built in
Quality checks ensure Cursor data is recent. Stale engineering metrics mean bad decisions, Bruin catches it.
YAML-defined, Git-versioned
Your Cursor pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
SQL + Python transforms
Calculate DORA metrics, MTTR, and custom KPIs with SQL or Python, in the same pipeline as ingestion.
Multi-source pipelines
Combine Cursor with Jira, GitHub, PagerDuty in one pipeline. Bruin resolves cross-source dependencies.

Before you start

Cursor team account

API key from team settings

Step 1

Add your Cursor connection

Connect using Cursor API key authentication. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyAPI key from your Cursor team settings

connections:
  cursor:
    type: cursor
    uri: "cursor://?api_key=<api-key-here>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Cursor and where to land it. This file lives in your Git repo, reviewable, version-controlled, and deployable with CI/CD.

Available tables

team_membersdaily_usage_datateam_spendfiltered_usage_events

name: raw.cursor_team_members
type: ingestr

parameters:
  source_connection: cursor
  source_table: 'team_members'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Cursor data. If a check fails, the pipeline stops, bad data never reaches downstream models or dashboards.

Validate data freshness, stale metrics get flagged

Ensure record IDs are unique across syncs

Catch records older than expected sync window

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: created_at
    checks:
      - name: not_null

custom_checks:
  - name: no stale records
    query: |
      SELECT COUNT(*) > 0
      FROM raw.cursor_team_members
      WHERE created_at > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)

Step 4

Run it

One command. Bruin connects to Cursor, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops, bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Cursor to your dashboards

$ bruin run .

Running pipeline...

  cursor_team_members
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Engineering integrations

Asana

Bitbucket

CircleCI

ClickUp

Datadog

Freshservice

Ready to connect Cursor?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs