CSV Files + Bruin

SourceDestination

Ingest data from CSV Files 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
CSV Files 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 CSV Files before it breaks models.
Data lake orchestration
Use CSV Files as a staging layer. Bruin handles landing, transforming, and materializing, all in one pipeline.
Multi-cloud flexibility
Move data between CSV Files and other storage or warehouses. Bruin manages scheduling, retries, and lineage.

For data & engineering teams

How it works

Automatic schema detection
Bruin detects CSV Files data schemas automatically. No manual configuration when formats change.
YAML-defined, Git-versioned
Your CSV Files 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 CSV Files at the ingestion layer.
Land, transform, materialize
Use CSV Files as staging. Bruin handles the full flow: land raw data, transform, and materialize into your warehouse.

Before you start

File system access

Step 1

Add your CSV Files connection

Direct file path to CSV file. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

pathFull path to the CSV file

connections:
  csv:
    type: csv
    uri: "csv://path/to/csv/file.csv"

Step 2

Create your pipeline

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

name: raw.csv_data
type: ingestr

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

Step 3

Add quality checks

Add column-level and custom SQL checks to your CSV Files 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.csv_data
      WHERE event_timestamp > CURRENT_TIMESTAMP()

Step 4

Run it

One command. Bruin connects to CSV Files, 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 CSV Files to your dashboards

$ bruin run .

Running pipeline...

  csv_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

Dropbox

Google Cloud Storage

Google Drive

Ready to connect CSV Files?

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