Pipedrive + Bruin

Source

Ingest Pipedrive 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 Pipedrive 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 Pipedrive data into forecasting models. Bad CRM data makes bad forecasts — Bruin catches issues first.
Marketing attribution that works
Connect Pipedrive 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 Pipedrive 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 Pipedrive data from ingestion through every transform to final dashboards. Know what breaks when schemas change.

Before you start

Pipedrive API token from account settings

Step 1

Add your Pipedrive connection

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

Parameters

api_keyPipedrive API token

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

Step 2

Create your pipeline

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

Available tables

dealspersonsorganizationsactivitiespipelinesusers

name: raw.pipedrive_deals
type: ingestr

parameters:
  source_connection: pipedrive
  source_table: 'deals'
  destination: bigquery

# Syncs contacts, deals, and activities
# with incremental loading and deduplication.

Step 3

Add quality checks

Validate Pipedrive data on every sync. Catch orphaned deals, duplicate contacts, and invalid pipeline stages before they corrupt your analytics.

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: email
    checks:
      - name: not_null
  - name: stage
    checks:
      - name: accepted_values
        value: ['lead', 'qualified', 'proposal', 'closed_won', 'closed_lost']

custom_checks:
  - name: no orphaned deals
    query: |
      SELECT COUNT(*) = 0
      FROM raw.pipedrive_persons deals
      LEFT JOIN raw.pipedrive_deals contacts
        ON deals.contact_id = contacts.id
      WHERE contacts.id IS NULL

Step 4

Run it

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

$ bruin run .

Running pipeline...

  pipedrive_deals
    ✓ 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

Apollo

Attio

Bullhorn

Clearbit

Ready to connect Pipedrive?

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