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DataQ

DataQ is a data-quality monitoring platform. It runs automated checks against your data — in Snowflake, ADLS Gen2, AWS S3 (or any S3-compatible store), Databricks Unity Catalog, and Apache Iceberg — tells you when something is wrong (failed checks, stale tables, unexpected row counts), and alerts your team. It watches your Azure Data Factory, Airflow, and dbt pipelines and can run checks automatically when a pipeline finishes.

Who it's for

  • Data engineers / SREs — author checks, wire up pipelines, triage failures.
  • QA / analysts — see what passed or failed and why.
  • Product & stakeholders — a health score and trend at a glance.

Quickstart (5 minutes)

Evaluate or self-host with no source checkout and no Azure tenant — just Docker:

curl -O https://raw.githubusercontent.com/TheurgicDuke771/DataQ/main/docker-compose.ghcr.yml
docker compose -f docker-compose.ghcr.yml up

Open http://localhost:3000. The stack comes up migrated and seeded with demo data, on dev-bypass auth (every request is a fixed demo user — no sign-in). API + Swagger at http://localhost:8000/docs. The GHCR images are multi-arch (amd64 + arm64, native on Apple Silicon) and all ports bind to 127.0.0.1 only.

Develop DataQ — from source

git clone https://github.com/TheurgicDuke771/DataQ.git
cd DataQ
./scripts/setup.sh        # conda env + pre-commit + docker-compose + migrations
conda activate dataq
docker-compose up         # Postgres + Redis + FastAPI + React + Celery worker
  • Backend API: http://localhost:8000 (interactive docs at /docs in dev).
  • Frontend: http://localhost:3000.

Then open the UI, add a connection, create a suite of checks, and run it. See Getting started for both paths in depth (incl. self-hosting with your own Azure AD) and Datasources & checks to author your first check.

Where to go next