Tech riderRev. 5 Oct 2026

Amazon Deequ

Ch 136.6of 26 Database Testing Tools

1Runs onAPI, Linux, Mac, self-hosted, Windows
2CostsFree plan
3Schema migration testsYes
4Data quality checksYes
5Test executionself hosted
6Test languageScala, Java, DQDL, SQL

6 lines stated Written from the maker's own pages: github.com

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Overview

Amazon Deequ is ranked #13 of 26 in database testing tools on Specifiction. It runs on API, Linux, macOS, Self-hosted, Windows. There is a free plan.

Amazon Deequ plans and pricing

All plans

Apache 2.0 open-source library Free Requires Apache Spark; release must match Spark version github.com · 5 Oct 2026

Compared on database testing tools

Schema migration tests: Yesgithub.com
Data quality checks: Yesgithub.com
Test execution: self_hostedgithub.com
Test language: Scala, Java, DQDL, SQLgithub.com

Facts

Purpose: Deequ is an Apache Spark library for defining data unit tests that measure quality in large datasets.github.com · 4 Oct 2026
Data scale: The project says Deequ is designed for very large datasets, including billions of rows, typically stored in a distributed filesystem or data warehouse.github.com · 4 Oct 2026
Checks: Checks can validate row counts, completeness, uniqueness, allowed values, nonnegative values, patterns, and approximate quantiles.github.com · 4 Oct 2026
Profiling and monitoring: Examples cover data profiling, persisting and querying computed metrics, anomaly detection over time, automatic constraint suggestions, and incremental metrics computation.github.com · 4 Oct 2026
DQDL: Deequ supports the declarative Data Quality Definition Language, including rules for counts, completeness, uniqueness, statistics, schema matching, freshness, and custom SQL.github.com · 4 Oct 2026
Row-level results: Row-level evaluation identifies rows that pass or fail supported rules, while dataset-level rules such as RowCount and Mean are marked as skipped.github.com · 4 Oct 2026
Compatibility: Deequ releases target specific Apache Spark versions; versions 2.1.0 and later require Java 11, and the README lists Spark 3.1 through 3.5 compatibility for Deequ 2.x.github.com · 4 Oct 2026
Installation: The README provides Maven and sbt dependency examples and directs users to select a release matching their Spark version.github.com · 4 Oct 2026
Python interface: The project points Python users to PyDeequ, described on its repository as a Python API for Deequ.github.com · 4 Oct 2026
AWS relationship: AWS Glue Data Quality documentation says that managed service is built on the open-source Deequ framework and uses DQDL.docs.aws.amazon.com · 4 Oct 2026
License: The library is licensed under Apache 2.0.github.com · 4 Oct 2026
Security reporting: The repository security policy asks users not to report security concerns in public GitHub issues and directs them to AWS's Vulnerability Disclosure Program or email.github.com · 4 Oct 2026
Contribution and feedback: The README welcomes feedback and contributions.github.com · 4 Oct 2026
Scale: The project says it is designed for very large datasets, including billions of rows, typically stored in distributed filesystems or data warehouses.github.com · 5 Oct 2026
Metrics and profiling: The project examples include metrics persistence and querying, data profiling, anomaly detection over time, automatic constraint suggestions, and incremental metric computation.github.com · 5 Oct 2026
Integrations: Deequ is built on Apache Spark, is distributed through Maven artifacts, and has a Python interface called PyDeequ.github.com · 5 Oct 2026
Support and contributions: The project welcomes feedback and contributions and directs bug reports and feature requests to its GitHub issue tracker.github.com · 5 Oct 2026
Intended use: The README describes using data checks to catch errors before datasets reach consuming systems or machine-learning algorithms.github.com · 5 Oct 2026