LaunchDarkly + Bruin

Source

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

Before you start

LaunchDarkly account

API access token with Reader role

Projects with feature flags

Step 1

Add your LaunchDarkly connection

Connect using LaunchDarkly API access token. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyLaunchDarkly API access token

connections:
  launchdarkly:
    type: launchdarkly
    uri: "launchdarkly://api_key"

Step 2

Create your pipeline

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

Available tables

feature_flagsenvironmentsprojectssegmentsaudit_log

name: raw.launchdarkly_feature_flags
type: ingestr

parameters:
  source_connection: launchdarkly
  source_table: 'feature_flags'
  destination: bigquery

Step 3

Add quality checks

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

Step 4

Run it

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

$ bruin run .

Running pipeline...

  launchdarkly_feature_flags
    ✓ 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

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Bitbucket

CircleCI

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Datadog

Ready to connect LaunchDarkly?

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