⚡ 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
Data validation made beautiful and powerful
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.
Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes
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.
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.
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.
Example API implementation for Data Caterer
data and pipeline testing with and for SQL
Simple DB Fixtures for Sails.js v1 (fake data for testing).
DataBridge Quality Control
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
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