Best 9 Data Quality Tools for Polars (2026)

Open-source Polars schema validation library by QuantCo that validates Polars DataFrames against dy.Schema classes with column types, nullability, and @dy.rule checks, optionally casting inputs.

Open-source Polars data modelling library built on Pydantic that validates Polars DataFrames against Pydantic-based models with dtypes, uniqueness, bounds, and custom Polars constraints.

Open-source DataFrame validation library that validates Polars DataFrames and LazyFrames against schemas, running data-level checks on DataFrames and schema-level checks on LazyFrames.

Open-source data validation library by Posit that runs validation plans on Polars tables, with steps that can compute Polars expressions before a check, and reports results as tables.

Open-source Polars data diff library by QuantCo that compares two Polars DataFrames by primary key, reporting schema differences, rows only on one side, and per-column value changes.

Open-source DataFrame comparison library from Capital One that compares two Polars DataFrames on join columns and reports row, column, and value differences in a pandas-like report.

Open-source profiling library with a Rust core that profiles an in-memory Polars DataFrame passed to dp.profile() and exports the results with to_polars().

Open-source data contracts engine that registers Polars DataFrames as in-memory DuckDB tables, so Soda data contracts run on them through DuckDB.

AI-native data quality platform built on data contracts that registers Polars DataFrames as in-memory DuckDB tables, so Soda data contracts run on them through DuckDB.