cuallee
Python library for data validation on DataFrame APIs including Snowflake/Snowpark, Apache/PySpark and Pandas/DataFrame.
Decision gist · record as of 2026-08-14
Yes, with conditions. Cuallee is worth installing if you work with multiple dataframe libraries and need a unified data quality API. The low install friction (pure Python, two dependencies), permissive Apache 2.0 license, and broad dataframe support make it accessible. However, the aging maintenance status (311 days since last release, last commit 2026-02-05) means you should verify that the specific dataframe versions you use are still supported in 0.15.4 before committing to production use. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- The package is dataframe-agnostic but each target dataframe library (PySpark, Snowpark, etc.) must be installed separately.
- Low friction: pure Python wheel with only two runtime dependencies (toolz, requests).
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive; you may use, modify, and distribute cuallee freely in commercial and private projects, provided you include a copy of the license and note any changes you make.
last release 2025-10-07 (311 days) · last repo commit 2026-02-05 · 248 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 116,975 downloads/mo, #12,186 on PyPI
Alternatives
Verify before relying
pip install cuallee
from cuallee import Check, CheckLevel
check = Check(CheckLevel.WARNING, "Completeness")
check.is_complete("id").is_unique("id").validate(df)- Whether all supported dataframe providers (PySpark 4.0.1, Snowpark 1.11.1, pandas 2.0.2, DuckDB 1.4.0, Polars 1.34.0, Daft 0.2.24) are tested and maintained in the current 0.15.4 release.
- Current performance characteristics relative to pydeequ—the description cites a benchmark but does not specify the test dataset size or hardware used.
- Status of the 'new version of validate output' mentioned as under construction in the documentation.
What it is and what it does
Cuallee is a data quality validation framework designed to work across multiple dataframe libraries—PySpark, Snowpark, DuckDB, Polars, Daft, and BigQuery—without requiring a separate implementation for each. It was built as a pure-Python alternative to pydeequ, addressing usability and performance concerns with that framework. The library lets you define checks for data integrity (completeness, uniqueness, patterns, ranges, statistical anomalies) and run them against your dataframe, returning pass/fail results with detailed metrics.
The package depends on toolz and requests, keeping its footprint minimal. It supports checks across multiple data types and includes specialized validators for dates, membership tests, regular expressions, and workflow sequences (process mining). You can compose checks into a fluent API, run them individually or as controls across entire dataframes, and get results as either detailed validation reports or simple boolean assertions.
Use it for
- Validate completeness and uniqueness on PySpark DataFrames before loading data into a warehouse.
- Run date range and continuity checks (e.g., is_daily) on time-series data in Snowpark or DuckDB.
- Test that categorical columns conform to an allowed set of values using is_contained_in across multiple dataframe backends.
- Detect statistical anomalies (e.g., interquartile range outliers) in numeric columns without writing custom aggregation logic.
- Verify business process workflows (e.g., Order-to-Cash sequences) using has_workflow to ensure event ordering and state transitions.
- Apply completeness checks across all columns in a dataframe using Control.completeness for quick data profiling.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Cuallee is worth installing if you work with multiple dataframe libraries and need a unified data quality API. The low install friction (pure Python, two dependencies), permissive Apache 2.0 license, and broad dataframe support make it accessible. However, the aging maintenance status (311 days since last release, last commit 2026-02-05) means you should verify that the specific dataframe versions you use are still supported in 0.15.4 before committing to production use. No known security vulnerabilities.
Install
cuallee on PyPI
Before you install
Low friction: pure Python wheel with only two runtime dependencies (toolz, requests). Maintenance status is aging—last release was 311 days ago and the last commit on 2026-02-05, though the repo remains active with 248 stars and is not archived.
Requires Python 3.10 or later. The package is dataframe-agnostic but each target dataframe library (PySpark, Snowpark, etc.) must be installed separately.
License in practice
Apache License 2.0 is permissive; you may use, modify, and distribute cuallee freely in commercial and private projects, provided you include a copy of the license and note any changes you make.
Quickstart
pip install cuallee
from cuallee import Check, CheckLevel
check = Check(CheckLevel.WARNING, "Completeness")
check.is_complete("id").is_unique("id").validate(df)Verify before relying
- Whether all supported dataframe providers (PySpark 4.0.1, Snowpark 1.11.1, pandas 2.0.2, DuckDB 1.4.0, Polars 1.34.0, Daft 0.2.24) are tested and maintained in the current 0.15.4 release.
- Current performance characteristics relative to pydeequ—the description cites a benchmark but does not specify the test dataset size or hardware used.
- Status of the 'new version of validate output' mentioned as under construction in the documentation.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestoolzrequests |
| Maintenance | Aging 311 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 116,975 / month, #12,186 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: cuallee-0.15.4-py3-none-any.whl
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