Airtable + Bruin

SourceDestination

Ingest data from Airtable or push enriched data back — with quality checks, lineage, and scheduling. Defined in YAML, version-controlled in Git.

For business teams

What you get

Operational analytics
Airtable data in your warehouse means analytics that Airtable's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Airtable with Jira, GitHub, Slack, and other tools. One dashboard that shows the real state of projects.
Team workload insights
Understand collaboration patterns, bottlenecks, and workload distribution from Airtable data — automatically updated.
No manual data pulling
Airtable data syncs on schedule. Managers and leads get fresh data without asking anyone.

For data & engineering teams

How it works

Incremental sync
Only sync new and changed Airtable records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Airtable pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Airtable schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Airtable data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Personal access token from Airtable

Step 1

Add your Airtable connection

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

Parameters

access_tokenA personal access token for authentication with the Airtable API

connections:
  airtable:
    type: airtable
    uri: "airtable://?access_token=<access_token>"

Step 2

Create your pipeline

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

Available tables

<base_id>/<table_id>

name: raw.airtable_<base_id>/<table_id>
type: ingestr

parameters:
  source_connection: airtable
  source_table: '<base_id>/<table_id>'
  destination: bigquery

Step 3

Add quality checks

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

Validate workspace data synced completely

Ensure record IDs are unique and titles are present

Catch missing or null fields on every sync

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

custom_checks:
  - name: workspace sync is complete
    query: |
      SELECT COUNT(*) > 0
      FROM raw.airtable_<base_id>/<table_id>

Step 4

Run it

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

$ bruin run .

Running pipeline...

  airtable_<base_id>/<table_id>
    ✓ 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 Productivity integrations

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Bitbucket

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Canny

ClickUp

Confluence

Ready to connect Airtable?

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