Side by side
Amazon Deequ vs Great Expectations
Both stand on data quality testing tools. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan APILinuxMac+2
Deequ offers a broad set of data checks and profiling tools for Spark-based pipelines, including declarative rules and row-level results. Teams need to match the release to their Spark version and account for the Java 11 requirement in newer releases.
Visit Amazon Deequ
Free plan APILinuxMac+2
Visit Great Expectations The facts
| Amazon Deequ | Great Expectations | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | API, Linux, Mac, Self-hosted, Windows | API, Linux, Mac, Self-hosted, Web |
| Lowest paid plan | Free or on request | Free or on request |
| Plans published | 1 | 3 |
| Maker | Not published | Not published |
| Website | github.com | greatexpectations.io |
| On data quality testing tools | #1 of 9 | #2 of 9 |
Amazon Deequ: what it does well
- Checks common data quality conditions and patterns.
- Designed for datasets that can reach billions of rows.
- Supports profiling, anomaly detection, and incremental metrics.
- DQDL includes schema, freshness, and custom SQL rules.
- Apache 2.0 licensed and free to use.
What to know first
- Requires Apache Spark and a release matching its version.
- Deequ 2.1.0 and later require Java 11.
- Dataset-level checks are skipped in row-level evaluation.
Great Expectations: what it does well
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