GitHub + Bruin

Source

Ingest GitHub 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 GitHub data with revenue and customer data. Show leadership how engineering reliability impacts the bottom line.
DORA metrics, automated
GitHub data feeds deployment frequency, lead time, MTTR, and change failure rate calculations automatically.
Catch data gaps
Quality checks ensure GitHub data is complete and fresh. Stale metrics mean bad decisions — Bruin catches it.
Cross-tool visibility
Combine GitHub 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 GitHub data is recent. Stale engineering metrics mean bad decisions — Bruin catches it.
YAML-defined, Git-versioned
Your GitHub 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 GitHub with Jira, GitHub, PagerDuty in one pipeline. Bruin resolves cross-source dependencies.

Before you start

GitHub personal access token

Repository access permissions

Step 1

Add your GitHub connection

Connect using GitHub personal access token. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

access_tokenGitHub personal access token
repositoryRepository in owner/repo format

connections:
  github:
    type: github
    uri: "github://?access_token=<access_token>&repository=<repository>"

Step 2

Create your pipeline

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

Available tables

issuespull_requestscommitsreleasescontributorsrepositories

name: raw.github_issues
type: ingestr

parameters:
  source_connection: github
  source_table: 'issues'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your GitHub 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.github_issues
      WHERE created_at > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)

Step 4

Run it

One command. Bruin connects to GitHub, 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 GitHub to your dashboards

$ bruin run .

Running pipeline...

  github_issues
    ✓ 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

Cursor

Datadog

Ready to connect GitHub?

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