Open source · MIT · self-hosted

Trust your data,
powered by DataQ

Freshness, volume, schema-drift and value checks across your warehouses, lakehouses and object stores — run the moment a pipeline finishes, routed to the team that owns the data, and open to your AI assistant over MCP.

Try it in 5 minutes

Snowflake · Databricks Unity Catalog · Apache Iceberg · ADLS Gen2 · AWS S3 (and any S3-compatible store) — watching Azure Data Factory, Airflow and dbt.

The DataQ monitoring dashboard: asset health, data-integrity score, pass rate, quality trend and per-suite performance

Everything between a bad load and a bad decision

Seven kinds of check, three orchestrators, every alert de-duplicated, and one assistant-ready interface — on infrastructure you run.

Checks that catch real incidents

Freshness, volume and schema-drift monitors notice a load that never arrived, arrived half-empty or changed shape. Twenty-five vetted Great Expectations types, custom SQL with a live dry-run, anomaly detection on a learned baseline, and cross-dataset comparison cover the rest.

orders · freshness

pass

orders · status in set

critical

orders · amount in range

warn

Runs when the pipeline does

Azure Data Factory, Airflow and dbt tell DataQ when a load finishes; the suite runs right after a success and the result is correlated to that pipeline run. Failures alert without running checks on nothing. Cron schedules cover everything else.

Alerts the right team, once

Microsoft Teams, Slack, email and webhooks, routed by severity and configured per suite. A broken check reports when it breaks and when it gets worse — not on every run.

Assets & lineage

Every table and file DataQ knows about, health rolled up per asset, lineage pulled from dbt, your catalog or the warehouse, incidents anchored where they hit.

Built-in assistant

Bring your own LLM: suggest checks from a column profile, turn a question into validated SQL, and get a root-cause narrative on a failing run.

Open to your AI tools

48 MCP tools expose the same actions to Claude, Copilot and Cursor — every one honest about what it cannot see, none of them touching a credential.

Secure by design

Workspace roles times per-suite grants, three sign-in modes, credentials in Key Vault, Secrets Manager or OpenBao, failing rows redacted, and a compliance pack for GDPR, CCPA and HIPAA reviews.

Runs where your data lives

Reference deployments on Azure Container Apps and AWS ECS, a one-command Docker stack for evaluation, no vendor lock-in behind any seam.

Documented like a product

Tutorials with screenshots and clips, a generated reference for every check type, endpoint and tool, forty-plus decision records, and pages your assistant can read as Markdown.

See it in action

Real screens from the seeded demo workspace — the same ones in the tutorials.

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

A suite with four checks, their data-quality dimensions, and the pipeline triggers panel — A suite: connection, target, checks with their dimension, triggers.

A run: per-check status, measured metric and observed value; a critical check has opened an incident — A run: per-check status, the measured metric, an opened incident.

The assets tree grouped by source with per-table health — Assets: every table and file, health rolled up per source.

Assistant: generate a curated set of checks for the suite from data insights.

Connection cards for Unity Catalog, ADLS Gen2, AWS S3 and Snowflake, plus Airflow and Azure Data Factory orchestration, each with its credential and environment status — Connections: every source and orchestrator, one connection manager.