Data Contracts engine for the modern data stack. https://www.soda.io

Code review for data in dbt

Data validation toolkit for assessing and monitoring data quality.

Great Expectations Airflow operator

re_data - fix data issues before your users & CEO would discover them 😊

A simple and easy to use Data Validation library for Python.

DataOps Data Quality TestGen is part of DataKitchen's Open Source Data Observability. DataOps TestGen delivers simple, fast data quality test generation and execution by data profiling,  new dataset hygiene review, AI generation of data quality validation tests, ongoing testing of data refreshes, & continuous anomaly monitoring

Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes

⚡ Prevent downstream data quality issues by integrating the Soda Library into your CI/CD pipeline.

Declarative data quality engine. Define checks in YAML, run anywhere.

This library is inspired by the Great Expectations library. The library has made the various expectations found in Great Expectations available when using the inbuilt python unittest assertions.

Spark Data Test - A PySpark-based automation testing utility to compare Spark DataFrames

data and pipeline testing with and for SQL

Library for data quality monitoring based on duckdb.

Data quality for dbt, without writing tests. Auto-generates dbt tests by profiling your database.

Controlled destruction for data pipelines

Schema-aware synthetic data for databases, APIs, and pipelines. Realistic, relational, privacy-safe.

This project implements a simple Linear Regression model from scratch and compares it to the implementation of scikit-learn, using the California Housing Dataset.

CLI to generate relational synthetic data with realistic chaos – nulls, duplicates, drift, and messy formatting.

Rage-quit your flaky DB regressions — modern, lightweight, multi-DB regression testing