Databricks + Bruin

SourceDestination

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

For business teams

What you get

100+ sources into ${pn}
Pull from any tool, database, or API directly into Databricks. One YAML file per source, all managed by Bruin.
Data quality you can trust
Column-level and custom SQL checks on any Databricks table. Bad data gets blocked before it reaches dashboards.
Full lineage visibility
Trace data from ingestion through transforms to final reports. When something breaks, find the cause in seconds.
SQL + Python in one pipeline
Build transforms in Databricks with both SQL and Python. Bruin resolves dependencies across languages automatically.

For data & engineering teams

How it works

100+ managed connectors
Ingest from any source directly into Databricks with one YAML file per source. Bruin manages connections and scheduling.
YAML-defined, Git-versioned
Every pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
SQL + Python assets
Build transformation layers in Databricks with SQL and Python. Bruin resolves dependencies and handles materialization.
Quality gates between stages
Quality checks run between ingestion and transformation. Bad data gets blocked before it reaches downstream models.

Before you start

Databricks workspace with SQL endpoint

Personal access token generated

SQL endpoint running (not terminated)

Appropriate permissions on catalog/schema

Step 1

Add your Databricks connection

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

Parameters

tokenPersonal access token (use as username)
hostWorkspace URL
portPort number (usually 443)
http_pathSQL endpoint HTTP path

connections:
  databricks:
    type: databricks
    uri: "databricks://token@host:port/http_path"

Step 2

Create your pipeline

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

Available tables

bronze.raw_datasilver.cleaned_datagold.aggregates

name: raw.databricks_bronze.raw_data
type: ingestr

parameters:
  source_connection: databricks
  source_table: 'bronze.raw_data'
  destination: bigquery

Step 3

Add quality checks

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

Validate data freshness on every sync

Ensure IDs are unique across tables

Block bad data before it reaches downstream models

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

custom_checks:
  - name: freshness check
    query: |
      SELECT MAX(updated_at) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
      FROM raw.databricks_bronze.raw_data

Step 4

Run it

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

$ bruin run .

Running pipeline...

  databricks_bronze.raw_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 Data Warehouse integrations

Amazon Redshift

AWS Athena

AWS EMR Serverless

GCP Dataproc Serverless

Google BigQuery

Microsoft Fabric

Ready to connect Databricks?

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