Data quality testing · cuallee (open-source project)

cuallee

Lightweight, DataFrame-agnostic Python library for writing data quality checks with one API across engines.

Visit cuallee Alternatives

cuallee is a pure-Python data quality library built as a faster, simpler alternative to PyDeequ, the Python wrapper around Amazon's Deequ. Checks (completeness, uniqueness, value ranges, pattern matching and more) are written once through a fluent, chainable API and can run unchanged against pandas, PySpark, Snowpark, DuckDB, Polars or Daft dataframes, which suits teams whose pipelines mix several of those engines rather than standardizing on one. It has no server component, UI or scheduler of its own: it is a library called from within existing scripts, notebooks or orchestration jobs, and results are returned as a dataframe of check outcomes for the caller to act on. Created in 2022 and published as a peer-reviewed paper in the Journal of Open Source Software in 2024, it is maintained as an open-source project under the Apache-2.0 license with no commercial tier.

At a glance

Vendor cuallee (open-source project)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Data engineers running checks across multiple dataframe engines who want one lightweight, code-first API.

Pricing

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

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

Features

  • Single fluent API for defining data quality checks
  • Runs unchanged across pandas, PySpark, Snowpark, DuckDB, Polars and Daft
  • Completeness, uniqueness, range and pattern checks
  • Faster, pure-Python alternative to PyDeequ
  • Results returned as a dataframe for downstream handling
  • No server, UI or scheduler required

Integrations

Profile last reviewed September 21, 2026

Alternatives

Pandera Open-source Python library that validates dataframes against a declared schema, catching bad data before it reaches a model or report. Great Expectations Open-source Python framework for defining, running and documenting data quality tests ("expectations") in pipelines. Soda Data quality testing platform combining a check-as-code language (SodaCL) with a SaaS UI for monitoring and collaboration.

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Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for.

Related guides

How to choose a data quality testing tool Pick by how checks get authored — code library or check-as-code platform — and whether you need scheduling, dashboards and alerting, or just a test suite.