ChartMogul + Bruin

Source

Ingest ChartMogul 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

Analysis beyond built-in reports
Join ChartMogul behavioral data with revenue, support, and CRM data. Answer questions ChartMogul alone can't.
Trusted behavioral data
Quality checks catch tracking gaps, duplicate events, and missing timestamps before they corrupt your models.
Self-serve for analysts
ChartMogul data lands in your warehouse where analysts already work. No more exporting, no more waiting.
Real user journeys
Combine ChartMogul events with purchase and support data to see the full customer journey, not just the product funnel.

For data & engineering teams

How it works

Event schema validation
Check for null event IDs, missing timestamps, and duplicate events on every sync. Catch tracking issues at ingestion.
YAML-defined, Git-versioned
Your ChartMogul pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
SQL + Python transforms
Transform raw ChartMogul events into funnels, cohorts, and user journeys with SQL or Python, in the same pipeline.
Dependency-aware scheduling
Bruin resolves pipeline dependencies automatically. Transforms only run after ChartMogul data has landed.

Before you start

ChartMogul API key

Step 1

Add your ChartMogul connection

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

Parameters

api_keyChartMogul API key for authentication

connections:
  chartmogul:
    type: chartmogul
    uri: "chartmogul://?api_key=<api_key>"

Step 2

Create your pipeline

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

Available tables

customerssubscriptionsinvoicesmrr

name: raw.chartmogul_customers
type: ingestr

parameters:
  source_connection: chartmogul
  source_table: 'customers'
  destination: bigquery

Step 3

Add quality checks

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

Catch duplicate events and missing timestamps

Validate event freshness, stale data gets flagged

Ensure event IDs are unique across syncs

columns:
  - name: event_id
    checks:
      - name: not_null
      - name: unique
  - name: event_timestamp
    checks:
      - name: not_null

custom_checks:
  - name: data is fresh
    query: |
      SELECT MAX(event_timestamp) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
      FROM raw.chartmogul_customers

Step 4

Run it

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

$ bruin run .

Running pipeline...

  chartmogul_customers
    ✓ 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 Analytics integrations

Amplitude

Apple App Store

Braze

CleverTap

Datadog

FullStory

Ready to connect ChartMogul?

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