Apollo + Bruin

Source

Ingest Apollo 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

Sales analytics beyond the CRM
Join Apollo pipeline data with marketing spend and product usage. Know which campaigns actually drive revenue.
Clean contact data
Quality checks deduplicate contacts, catch missing emails, and validate pipeline stages on every sync.
Revenue forecasting you trust
Feed clean Apollo data into forecasting models. Bad CRM data makes bad forecasts — Bruin catches issues first.
Marketing attribution that works
Connect Apollo closed-won deals back to ad spend and campaigns. Finance gets numbers they can actually trust.

For data & engineering teams

How it works

Deduplication built in
Bruin handles incremental loading with merge strategy. Contacts and deals are deduplicated automatically on every sync.
YAML-defined, Git-versioned
Your Apollo pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Custom SQL quality checks
Validate pipeline stage values, check for orphaned deals, and enforce referential integrity with custom SQL.
End-to-end lineage
Trace Apollo data from ingestion through every transform to final dashboards. Know what breaks when schemas change.

Before you start

Apollo.io account

API key from Settings > Integrations

Step 1

Add your Apollo connection

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

Parameters

api_keyApollo.io API key from Settings

connections:
  apollo:
    type: apollo
    uri: "apollo://api_key"

Step 2

Create your pipeline

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

Available tables

contactsaccountssequencesemailscallstasks

name: raw.apollo_contacts
type: ingestr

parameters:
  source_connection: apollo
  source_table: 'contacts'
  destination: bigquery

Step 3

Add quality checks

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

Validate pipeline stage values against accepted list

Catch orphaned deals with no contact attached

Ensure contact emails are never null

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: stage
    checks:
      - name: accepted_values
        value: ['lead', 'qualified', 'proposal', 'negotiation', 'closed_won', 'closed_lost']

custom_checks:
  - name: no orphaned deals
    query: |
      SELECT COUNT(*) = 0
      FROM raw.apollo_contacts
      WHERE contact_id IS NULL

Step 4

Run it

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

$ bruin run .

Running pipeline...

  apollo_contacts
    ✓ 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 CRM & Sales integrations

ActiveCampaign

Attio

Bullhorn

Clearbit

Copper

Ready to connect Apollo?

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