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DataQ

Know your data is right before anyone else finds out it isn't. DataQ runs automated checks against your tables and files — in Snowflake, Databricks Unity Catalog, Apache Iceberg, ADLS Gen2, AWS S3 or any S3-compatible store — tells you when something is wrong, and alerts the team that owns it. It watches your Azure Data Factory, Airflow and dbt pipelines and runs the checks the moment a load finishes.

A ten-second tour: dashboard, assets, connections, suites, results.

  • Try it in five minutes

    One docker compose up, no cloud account, no identity provider. Sign in with an emailed code and explore seeded demo data.

    Install

  • Learn it in an hour

    Four short tutorials: your first suite, your first alert, running it automatically, and asking an AI assistant.

    Get started

  • Look something up

    Every check type, every REST endpoint, every MCP tool — generated from the code, so it cannot drift.

    Reference

What you can do with it
Catch the incidents that page people Freshness, volume and schema-drift monitors notice a load that did not arrive, arrived half-empty, or changed shape — before a value-level rule ever runs.
Author precise checks without code Twenty-five vetted Great Expectations types, custom SQL with a live dry-run, comparisons against a second dataset, anomaly detection on a learned baseline. Let the built-in assistant suggest checks from a column profile.
See the whole estate An asset view rolls health up per table, with lineage pulled from dbt, your catalog or the warehouse itself, and incidents anchored to the asset they hit.
Alert the right people, once Teams, Slack, email and webhooks, routed by severity, de-duplicated so a broken check reports when it breaks, not on every run.
Run it where the data lives Reference deployments on Azure Container Apps and AWS ECS; a single-host Docker stack for evaluation; bring your own identity provider or none at all.
Let assistants do the work Forty-eight MCP tools expose the same actions to Claude, Copilot and Cursor, every one honest about what it cannot see.

Who it is for

  • Data engineers and SREs author checks, wire pipelines, triage failures.
  • Analysts and QA see what passed or failed and why, with redacted failing rows.
  • Stakeholders get a health score and a trend, not a Slack thread.

Quickstart

curl -O https://raw.githubusercontent.com/TheurgicDuke771/DataQ/main/docker-compose.ghcr.yml
export OPENBAO_TOKEN=$(openssl rand -hex 16)     # root token for the bundled vault
export DATAQ_SIGNIN_EMAIL=you@example.com        # the address allowed to sign in
docker compose -f docker-compose.ghcr.yml up

Open http://localhost:3000, type the address you exported, and read the six-digit code in the bundled inbox at http://localhost:8025. The stack comes up migrated and seeded with demo data; nothing leaves your machine. Full flow, other sign-in modes and the from-source path: Install.

Where next