Datadog + Bruin

Source

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

Before you start

Datadog account

API key and application key

Read access to required data

Step 1

Add your Datadog connection

Connect using Datadog API and application keys. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyDatadog API key
app_keyDatadog application key
siteDatadog site (e.g., datadoghq.com, datadoghq.eu)

connections:
  datadog:
    type: datadog
    uri: "datadog://api_key:app_key@site"

Step 2

Create your pipeline

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

Available tables

metricseventsmonitorsdashboardslogstraces

name: raw.datadog_metrics
type: ingestr

parameters:
  source_connection: datadog
  source_table: 'metrics'
  destination: bigquery

Step 3

Add quality checks

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

Step 4

Run it

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

$ bruin run .

Running pipeline...

  datadog_metrics
    ✓ 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

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Ready to connect Datadog?

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