Amazon Deequ

Overview

Amazon Deequ is an open-source library for testing data quality in Apache Spark pipelines. It is best suited to engineering teams that already run Spark and want checks and metrics to catch errors before data reaches downstream systems or machine-learning workloads. Its range of validation and profiling options is useful, but it is not a fit for teams seeking a standalone service independent of Spark.

Key features

Deequ checks row counts, completeness, uniqueness, allowed and nonnegative values, patterns, and approximate quantiles. That covers common quality problems across structured datasets, and its Spark foundation is designed to work at very large scale, including billions of rows. In practice, the scale advantage matters most when data already lives in distributed filesystems or warehouses; it does not remove the need to manage a compatible Spark environment.

Its examples also cover data profiling, saving and querying metrics, anomaly detection over time, automatic constraint suggestions, and incremental metric computation. These workflows can extend Deequ beyond a one-time gate into recurring monitoring, though teams need to build them into their own Spark environment. DQDL offers declarative rules for counts, completeness, uniqueness, statistics, schema matching, freshness, and custom SQL, giving teams a way to express checks without writing each as imperative code.

Row-level evaluation can identify records that pass or fail supported rules, but dataset-wide rules such as RowCount and Mean are skipped in that mode. Teams needing per-record diagnostics should account for that boundary when designing their checks.

Pricing

Deequ's Apache 2.0 open-source library plan costs nothing. It includes the library without a paid tier, and the Apache 2.0 license permits use under its terms. The tradeoff is infrastructure: the plan requires Apache Spark, and each Deequ release must match the Spark version in use. There is no paid managed tier in this plan, so teams should choose it when they can operate the execution environment themselves.

Platforms

Deequ supports API, Linux, macOS, Windows, and self-hosted use. It is distributed through Maven artifacts, with Maven and sbt dependency examples, and its test languages include Scala, Java, DQDL, and SQL. Releases target specific Spark versions: Deequ 2.x lists Spark 3.1 through 3.5 compatibility, and releases 2.1.0 and later require Java 11. Python teams can use PyDeequ, the project's Python API. Deequ supports schema migration tests and custom detection rules, plus pre-commit scanning; it does not offer CI/CD scanning, pull-request scanning, or push protection. Git is the supported VCS.

Who it's for

Deequ suits data engineers and teams building Spark-based quality gates or recurring metrics for large datasets. It is particularly relevant when Scala or Java code, DQDL, or SQL checks can live alongside existing Spark jobs. Teams without Spark, or those looking for managed execution or repository scanning in CI and pull requests, should consider a different tool.

Pros and cons

• Pros: Broad checks cover common completeness, validity, uniqueness, and distribution needs, while profiling and anomaly-detection examples support ongoing monitoring.
• Pros: The Apache 2.0 library is free to use, and distributed Spark processing is designed for datasets at very large scale.
• Cons: Spark is required and release compatibility must be managed, adding operational and version-selection work.
• Cons: Row-level evaluation skips dataset-level rules such as RowCount and Mean, and the feature set does not include CI/CD or pull-request scanning.

Alternatives

DataKitchen TestGen is worth considering for a freemium option with an open-source plan that includes one user, one connection, and one project, alongside unlimited tables and data volume.

Great Expectations may suit teams that prefer a freemium tool with a Developer plan capped at five data assets under test per month and three users, while allowing unlimited rows per asset.

Atlas is an alternative for readers looking for an open-core database migration tool.

Soda is a freemium option whose Free plan includes pipeline testing, metrics observability, and alerting and ticketing integrations, with free Soda Processing Units.

SQLancer is another free software option to consider.

utPLSQL is a free alternative focused on unit testing Oracle Database PL/SQL code.

pgroll is a free alternative for PostgreSQL 14.0 or later, distributed as a single cross-platform binary.

SQLFluff is a free open-source SQL linter if the need is SQL linting rather than Spark data-quality testing.

Browse Database Testing Tools for more options, or Secrets Scanning Software for a different software category.

Verdict

Choose Amazon Deequ if you already run Spark and want a free, Apache 2.0 toolkit that combines large-scale data checks with profiling and metrics workflows. Its breadth is the main reason to choose it; the Spark dependency, version matching, and gaps in CI and pull-request scanning are the main reasons to look elsewhere.