Amazon Deequ

Overview

Amazon Deequ is a data-quality testing library built for Apache Spark. It suits engineering teams that already run Spark and want to catch dataset problems before data reaches downstream systems or machine-learning workloads. Its breadth is useful, but Spark is a firm prerequisite rather than an optional integration.

Key features

Deequ checks row counts, completeness, uniqueness, allowed and nonnegative values, patterns, and approximate quantiles. That makes it a practical toolkit for validating common data assumptions across large datasets, including workloads the project targets at billions of rows. The scale is most relevant to teams whose data already sits in distributed filesystems or warehouses; Deequ does not replace the work of operating a compatible Spark environment.

Beyond individual checks, example workflows cover profiling, storing and querying metrics, anomaly detection over time, automatic constraint suggestions, and incremental metric computation. Together these can support recurring quality monitoring, though teams must incorporate the workflows into their own Spark jobs. DQDL offers declarative rules for counts, completeness, uniqueness, statistics, schema matching, freshness, and custom SQL, giving teams another way to express validation requirements.

Row-level evaluation can identify records that pass or fail supported rules, which is useful when a pipeline needs to isolate problematic rows. Dataset-level checks such as RowCount and Mean are skipped in that mode, so row-level output cannot stand in for every aggregate validation.

Deequ releases are tied to specific Spark versions: the project lists Spark 3.1 through 3.5 for Deequ 2.x, and releases from 2.1.0 require Java 11. Maven and sbt dependency examples are provided, but selecting a matching release is an essential setup step. Scala, Java, DQDL, and SQL are supported test languages; Python users can turn to PyDeequ, a separate Python API for Deequ.

Pricing

Deequ is an Apache 2.0 open-source library, priced at free. There is no paid tier to unlock its listed data-quality checks or schema migration tests. The trade-off is that the library requires Apache Spark, and its release must match the Spark version in use.

Platforms

Deequ is available for API, Linux, macOS, self-hosted, and Windows environments. It runs as a Spark library distributed through Maven artifacts, with sbt dependency examples as well. Git is the supported VCS, and pre-commit scanning is supported; CI/CD scanning, pull-request scanning, and push protection are not.

Who it's for

Deequ is a strong fit for Spark teams that want configurable checks, profiling, and metrics workflows without adopting a paid platform. Custom detection rules and DQDL can help teams express their own data constraints, while schema migration tests suit workflows where schema changes need validation. It is less suitable for teams without Spark or those seeking built-in CI/CD or pull-request scanning.

Pros and cons

Broad validation coverage: checks include common completeness, uniqueness, value, pattern, count, and quantile expectations.

Designed for large-scale data: Spark execution targets datasets with billions of rows in distributed storage environments.

Multiple monitoring workflows: profiling, metric persistence, anomaly detection, suggestions, and incremental computation go beyond one-off checks.

Version coupling: Spark compatibility must be matched, and newer releases impose Java 11 as an additional requirement.

Aggregate checks have a row-level limit: RowCount and Mean are skipped during row-level evaluation.

No CI/CD or pull-request scanning: teams needing those scanning modes must look elsewhere.

Alternatives

Atlas is worth considering when database migration tooling is the priority; it has a free plan and free trial, with an open-core Apache 2.0 engine and advanced features available in a proprietary edition. DataKitchen TestGen may suit teams seeking a freemium database-testing option with a free forever open-source tier capped at one user, one connection, and one project, while allowing unlimited tables and data volume.

Great Expectations is another freemium choice: its Developer plan allows up to five data assets under test per month and three users, with unlimited rows per asset. Redgate SQL Provision is a paid option for provisioning or masking production data, with capacity tiers based on total production data volume. Bytebase Enterprise offers a free Community plan for up to 20 users and 10 database instances, so it may fit teams prioritizing database-instance management.

QuerySurge is a paid alternative with a free trial and user-pack plans, while Soda offers a free tier with pipeline testing, metrics observability, and alerting and ticketing integrations. ContextQA Database Testing is a paid, usage- and outcomes-based option sized to the testing footprint.

For broader directories, see Database Testing Tools and Secrets Scanning Software.

Verdict

Choose Deequ if your team already operates Apache Spark and needs a free, extensible way to validate and profile large datasets. Its varied checks and monitoring examples are the main draw; the dependency on Spark version alignment and the lack of CI/CD or pull-request scanning are the clearest reasons to choose another tool.