Iterable + Bruin

Source

Ingest Iterable 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

Marketing impact on revenue
Join Iterable engagement data with CRM deals and payments. Measure what marketing actually drives, not just opens and clicks.
Single source of truth
Combine Iterable with all your marketing channels in one warehouse. One dashboard, one set of numbers, no more spreadsheet reconciliation.
Clean audience data
Quality checks catch duplicate contacts, invalid emails, and bounce rate spikes before they affect campaigns.
Automated reporting
Stakeholders get fresh Iterable data every morning. No one needs to pull reports or wait for a data team.

For data & engineering teams

How it works

Incremental loading
Only sync new and updated Iterable records. No full reloads, no wasted compute, no duplicate contacts.
YAML-defined, Git-versioned
Your Iterable pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Email and contact validation
Quality checks catch null emails, duplicate contacts, and invalid data before it enters your warehouse.
Cross-source dependency resolution
Bruin resolves dependencies between Iterable and other sources automatically. Transforms run in the right order.

Before you start

Iterable account with API access

API key with data export permissions

Step 1

Add your Iterable connection

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

Parameters

api_keyIterable standard API key with export permissions

connections:
  iterable:
    type: iterable
    uri: "iterable://api_key"

Step 2

Create your pipeline

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

Available tables

userseventscampaignstemplateslistsmessage_types

name: raw.iterable_users
type: ingestr

parameters:
  source_connection: iterable
  source_table: 'users'
  destination: bigquery

Step 3

Add quality checks

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

Catch duplicate contacts before they enter your warehouse

Validate email fields are never null

Ensure record IDs are unique across syncs

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

custom_checks:
  - name: no duplicate contacts
    query: |
      SELECT COUNT(*) = COUNT(DISTINCT email)
      FROM raw.iterable_users

Step 4

Run it

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

$ bruin run .

Running pipeline...

  iterable_users
    ✓ 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 Marketing integrations

ActiveCampaign

Adjust

AdMob

AdRoll

Amazon Ads

Amplitude

Ready to connect Iterable?

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