Google Sheets + Bruin

Source

Ingest Google Sheets 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

Operational analytics
Google Sheets data in your warehouse means analytics that Google Sheets's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Google Sheets 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 Google Sheets data — automatically updated.
No manual data pulling
Google Sheets 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 Google Sheets records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Google Sheets pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Google Sheets schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Google Sheets data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Google account with Sheets API enabled

Service account created in Google Cloud

Sheet shared with service account email

Sheets API enabled in GCP project

Step 1

Add your Google Sheets connection

Google Sheets connection using service account. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

credentials_pathService account JSON file
spreadsheet_idID from sheet URL
sheet_nameName of specific sheet tab

connections:
  googlesheets:
    type: googlesheets
    uri: "googlesheets://credentials_path@spreadsheet_id/sheet_name"

Step 2

Create your pipeline

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

Available tables

Sheet1DataReportImport

name: raw.googlesheets_Sheet1
type: ingestr

parameters:
  source_connection: googlesheets
  source_table: 'Sheet1'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Google Sheets 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.googlesheets_Sheet1

Step 4

Run it

One command. Bruin connects to Google Sheets, 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 Google Sheets to your dashboards

$ bruin run .

Running pipeline...

  googlesheets_Sheet1
    ✓ 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

Airtable

Asana

Bitbucket

Calendly

Canny

ClickUp

Ready to connect Google Sheets?

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