Plaid + Bruin
Ingest Plaid 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
Revenue reporting you can audit
Plaid transaction data flows into your warehouse with quality checks that validate amounts, currencies, and reconciliation — every single sync.
MRR/ARR calculated right
Combine Plaid with subscription data to automate MRR, ARR, and churn calculations. Finance gets numbers, not guesswork.
Catch issues before close
Quality checks flag missing transactions, amount mismatches, and anomalies. Finance finds out from Bruin, not from the CFO.
Unified financial view
Join Plaid with your ERP, CRM, and other financial tools. One source of truth for revenue, not five spreadsheets.
For data & engineering teams
How it works
Idempotent incremental loads
Re-runs are safe. Bruin's merge strategy ensures Plaid transactions are never duplicated, even on retry.
YAML-defined, Git-versioned
Your Plaid pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert. Auditors love this.
Reconciliation checks
Custom SQL checks validate that amounts balance and currencies match. Pipeline stops if something doesn't add up.
Multi-destination support
Land Plaid data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.
Before you start
Step 1
Add your Plaid connection
Connect using Plaid API client credentials. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.
Parameters
client_idPlaid API client ID from the Plaid dashboardsecretPlaid API secret for the specified environmentenvironmentPlaid environment (sandbox, development, production)
connections:
plaid:
type: plaid
uri: "plaid://?client_id=<your-client-id>&secret=<your-secret>&environment=<sandbox|development|production>"Step 2
Create your pipeline
Define a YAML asset that tells Bruin what to pull from Plaid and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.
Available tables
name: raw.plaid_accounts
type: ingestr
parameters:
source_connection: plaid
source_table: 'accounts'
destination: bigqueryStep 3
Add quality checks
Add column-level and custom SQL checks to your Plaid data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.
columns:
- name: transaction_id
checks:
- name: not_null
- name: unique
- name: amount
checks:
- name: not_null
- name: currency
checks:
- name: not_null
custom_checks:
- name: amounts balance
query: |
SELECT ABS(SUM(credit) - SUM(debit)) < 0.01
FROM raw.plaid_accountsStep 4
Run it
One command. Bruin connects to Plaid, 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.
--start-date$ bruin run .Running pipeline...
plaid_accounts
✓ 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 12sReady to connect Plaid?
Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.