Data quality testing · DQOps

DQOps

Data quality and observability platform with 150+ built-in checks, configurable via UI or YAML, with daily automated monitoring.

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DQOps ships a large library of prebuilt data quality checks - completeness, validity, freshness, anomaly detection and more - that can be configured either through a web UI or as YAML files kept in version control, so teams can start from templates instead of writing every rule. Checks run on a schedule and results feed built-in dashboards and a data quality KPI score per table, with issue tracking for failures. Custom checks can be added in Python or SQL where the built-in library falls short. DQOps can be self-hosted, including on-premises or in a hybrid setup for organizations that cannot use a SaaS tool, or run as a managed cloud service. Its source is published on GitHub under the Business Source License 1.1 (converting to Apache-2.0 four years after each release), so it is source-available rather than OSI-approved open source. Published monthly pricing for the self-serve tiers is unusual in this market.

At a glance

Vendor DQOps
Pricing model Subscription
Free tier —
Deployment Cloud, Self-hosted
Open source No (BSL-1.1)
Best for Data teams wanting an out-of-the-box checks library with predictable per-table pricing.

Pricing

Personal and Team tiers have published monthly prices (billed annually); Enterprise is custom.

Plan Price Notes
Personal $600/month (billed annually) 200 monitored tables, 1 user, 2 concurrent jobs
Team $2,000/month (billed annually) 2,000 monitored tables, 5 users, 20 concurrent jobs
Enterprise Custom custom table/user limits; SaaS, on-premise or hybrid deployment

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • 150+ built-in data quality checks
  • YAML or UI-based configuration
  • Daily automated monitoring and dashboards
  • Anomaly detection
  • Data quality KPI scoring
  • Incident and issue tracking
  • Source-available self-hosting
  • Custom Python/SQL checks

Integrations

Profile last reviewed September 21, 2026

Alternatives

Great Expectations Open-source Python framework for defining, running and documenting data quality tests ("expectations") in pipelines. Soda Data quality testing platform combining a check-as-code language (SodaCL) with a SaaS UI for monitoring and collaboration. Deequ Open-source Scala/Spark library from AWS for defining unit tests that measure data quality on large datasets.

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Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior.

Related guides

How to choose a data quality testing tool Pick by how checks get authored — code library or check-as-code platform — and whether you need scheduling, dashboards and alerting, or just a test suite.