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Deequ vs Great Expectations

Deequ is a Spark-native library for teams already on AWS Glue or EMR; Great Expectations is engine-flexible with a bigger ecosystem and an optional hosted UI.

Side by side

Deequ Great Expectations
Vendor Amazon Web Services (AWS Labs) GX (Great Expectations)
Pricing model Open source + paid options Free tier + paid plans
Free tier Yes Yes
Deployment Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0)
Best for Spark-based data engineering teams on AWS wanting code-native quality checks at scale. Data and ML engineering teams wanting code-first, version-controlled data quality tests.
Pricing

Free, open-source library with no vendor pricing or hosted product.

Pricing has not been verified yet — see the vendor's site.

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

Developer Free
Team Custom
Enterprise Custom

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

Features
  • Declarative data quality 'unit tests'
  • Automated constraint suggestion
  • Anomaly detection on quality metrics over time
  • Incremental metric computation on Spark
  • PyDeequ Python API
  • AWS Glue and EMR integration
  • Data profiling
  • 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)

Verdict

Both are free, open-source Python-callable libraries with no server of their own — you embed either one in a pipeline and decide yourself how results get surfaced. The choice mostly comes down to what engine your data already runs on and how much ecosystem you want around the tool.

Deequ is built directly on Apache Spark: constraints are computed incrementally so checks stay cheap as a dataset grows, and its automatic constraint-suggestion and built-in anomaly detection track quality metrics over time. It is a natural fit for teams already running Spark jobs on AWS Glue, EMR or Databricks, since that is exactly the environment it was built for.

Great Expectations works against pandas, Spark or SQL-based warehouses through one consistent way of declaring checks ("expectations"), auto-generates human-readable documentation from the results, and integrates with orchestrators like Airflow, dbt and Dagster as a pipeline step. It also has a much larger community and, if you outgrow the pure-library workflow, an optional hosted layer (GX Cloud) with a free plan to start.

Choose Deequ if

  • Your pipelines already run on Apache Spark, particularly on AWS Glue or EMR.
  • You want drift detection on metrics over time rather than only fixed-threshold checks.
  • You are comfortable writing Scala or using the PyDeequ Python wrapper.

Choose Great Expectations if

  • Your data lives across more than one engine — pandas here, a warehouse there — and you want one way of declaring checks across both.
  • Auto-generated documentation of what was tested, and why, matters for your team or your auditors.
  • You want deep integration with Airflow, dbt or Dagster, and the option to add a hosted UI later without switching tools.

What they share

Neither ships with alerting, a dashboard or a scheduler on its own — both are meant to be called from inside pipelines you already orchestrate. Both are Apache-2.0 licensed, so there is no cost and no relicensing risk to plan around. And both can profile a dataset to suggest a starting set of checks rather than requiring every rule to be hand-written from a blank page.

Last reviewed September 22, 2026

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