data-testing

⚡ 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

Various files useful for manual testing and test automation etc.

Great Expectations Airflow operator

Data validation made beautiful and powerful

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

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

⚡ 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.

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.

data and pipeline testing with and for SQL

Data generation and validation tool for any data source

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

Example API implementation for Data Caterer

Documentation for Data Caterer

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

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