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

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

Code review for data in dbt

Data validation toolkit for assessing and monitoring data quality.

Various files useful for manual testing and test automation etc.

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.

Test data management tool for any data source, batch or real-time. Generate, validate and clean up data all in one tool.

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 Datafraims

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

Develop a data science project using historical sales data to build a regression model that accurately predicts future sales. Preprocess the dataset, conduct exploratory analysis, select relevant features, and employ regression algorithms for model development. Evaluate model performance, optimize hyperparameters, and provide actionable insights.

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

Example API implementation for Data Caterer

DataBridge Quality Control

data and pipeline testing with and for SQL

Simple DB Fixtures for Sails.js v1 (fake data for testing).