Data quality testing · GX (Great Expectations)

Great Expectations

Open-source Python framework for defining, running and documenting data quality tests ("expectations") in pipelines.

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Great Expectations lets teams declare data quality checks as "expectations" - for example that a column is never null, or that values fall within a range - in Python or YAML, then run them as part of a pipeline and get auto-generated human-readable documentation and validation results out the other side. It can profile a dataset to suggest a starting set of expectations rather than requiring every check to be written from scratch, and it integrates with orchestrators like Airflow and Dagster so validation becomes a pipeline step rather than a separate process. The core library is open-source under Apache-2.0 and works with Pandas, Spark and SQL-based warehouses. GX Cloud, the company's hosted product, adds a web UI, collaboration and monitoring on top, with a free Developer plan and quote-based Team and Enterprise plans.

At a glance

Vendor GX (Great Expectations)
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Data and ML engineering teams wanting code-first, version-controlled data quality tests.

Pricing

Open-source library is free; GX Cloud has a free Developer plan plus quote-based Team and Enterprise plans.

Plan Price Notes
Developer Free GX Cloud, for individuals
Team Custom contact required for pricing; adds collaboration features
Enterprise Custom contact sales

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

Features

  • Declarative 'expectations' for data validation
  • Auto-generated data documentation
  • Automated profiling and test suite generation
  • Airflow, dbt and Dagster integration
  • Python and SQL-based validation
  • GX Cloud hosted UI
  • Validation results store
  • Alerting on failed checks (Cloud)

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

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

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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 pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data validation Checking that data meets explicit, predefined rules for type, range, format, or consistency before it is used or trusted.

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.