{"categories":[{"label":"Quality Assurance","url":"https://skillfed.io/packages/category/software-development-quality-assurance/4"}],"enrichment":{"capability":"Cuallee provides a dataframe-agnostic API to define and run data quality checks across PySpark, pandas, Snowpark, DuckDB, Polars, Daft, and BigQuery, replacing pydeequ with a pure-Python implementation.","skillfed_tags":["data-quality","dataframe-validation","multi-backend"],"use_cases":["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."],"what_it_does":"Cuallee is a data quality validation framework designed to work across multiple dataframe libraries\u2014PySpark, Snowpark, DuckDB, Polars, Daft, and BigQuery\u2014without 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.\n\nThe 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.","worth_installing":"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."},"id":"cuallee","links":{"html":"https://skillfed.io/packages/cuallee","md":"https://skillfed.io/packages/cuallee.md","pypi":"https://pypi.org/project/cuallee/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-07","license_spdx":null,"license_treatment":"permissive","name":"cuallee","python_support":"supports_current","summary":"Python library for data validation on DataFrame APIs including Snowflake/Snowpark, Apache/PySpark and Pandas/DataFrame."},"popularity":{"monthly_downloads":116975,"position":12186,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.15.4"}