⚡ Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io

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

Code review for data in dbt

Great Expectations Airflow operator

Various files useful for manual testing and test automation etc.

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

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

DataOps 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 screening and hygiene review, algorithmic generation of data quality validation tests, ongoing testing of new data refreshes, & continuous data anomaly monitoring

Test data management tool for any data source, batch or real-time

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

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.

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

data and pipeline testing with and for SQL

Software Testing in Open Source and Data Science: A talk delivered at the Data Umbrella speaker series

Data generation and validation tool for any data source

Example API implementation for Data Caterer

Documentation for Data Caterer

Example API implementation for Data Caterer

A sample repository showcasing, implementation of testing for ETL pipeline developed with Apache Spark

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.