Google Cloud Storage + Bruin

SourceDestination

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

For business teams

What you get

Files and events in your warehouse
Google Cloud Storage data lands in your warehouse with automatic schema detection. No manual parsing, no format guessing.
Schema drift protection
Quality checks catch unexpected format changes, null values, and schema drift from Google Cloud Storage before it breaks models.
Data lake orchestration
Use Google Cloud Storage as a staging layer. Bruin handles landing, transforming, and materializing — all in one pipeline.
Multi-cloud flexibility
Move data between Google Cloud Storage and other storage or warehouses. Bruin manages scheduling, retries, and lineage.

For data & engineering teams

How it works

Automatic schema detection
Bruin detects Google Cloud Storage data schemas automatically. No manual configuration when formats change.
YAML-defined, Git-versioned
Your Google Cloud Storage pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Format validation
Quality checks catch schema drift, unexpected nulls, and format changes from Google Cloud Storage at the ingestion layer.
Land, transform, materialize
Use Google Cloud Storage as staging. Bruin handles the full flow: land raw data, transform, and materialize into your warehouse.

Before you start

Google Cloud service account

Storage Admin or appropriate bucket permissions

Step 1

Add your Google Cloud Storage connection

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

Parameters

credentials_pathPath to Google Cloud service account JSON file
project_idGoogle Cloud Project ID

connections:
  gcs:
    type: gcs
    uri: "gcs://?credentials_path=<credentials_path>&project_id=<project_id>"

Step 2

Create your pipeline

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

name: raw.gcs_data
type: ingestr

parameters:
  source_connection: gcs
  source_table: 'data'
  destination: bigquery

Step 3

Add quality checks

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

Catch events with future timestamps

Validate file paths and timestamps are present

Flag schema drift at the ingestion layer

columns:
  - name: file_path
    checks:
      - name: not_null
  - name: event_timestamp
    checks:
      - name: not_null

custom_checks:
  - name: no events from the future
    query: |
      SELECT COUNT(*) = 0
      FROM raw.gcs_data
      WHERE event_timestamp > CURRENT_TIMESTAMP()

Step 4

Run it

One command. Bruin connects to Google Cloud Storage, 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 Cloud Storage to your dashboards

$ bruin run .

Running pipeline...

  gcs_data
    ✓ 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 Storage & Streaming integrations

Amazon Kinesis

Amazon S3

Apache Kafka

CSV Files

Dropbox

Google Drive

Ready to connect Google Cloud Storage?

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