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:zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
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 😊
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
:zap: 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.
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
National Grid ( Python, SQL Server, SSIS, SSRS, Tableau, Power BI, SQL Server Import Export Wizard, Data Validations, Data Integrations, Data Conversions )
Translating between two sets of notation for Kalman filters
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.
Dynamic data testing engine based on pySpark
This project creates machine learning models capable of classifying candidate exoplanets from the raw dataset from NASA Kepler Space Telescope
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.
I'm learning how to use dbt with BigQuery so I can apply that knowledge wherever we end up working. It seems like a good DWH interface tool to know for data transformation and testing, and allows me to solidify concepts of testing in data ops.