There seems to be a trend where companies are creating tools to monitor data quality... * Great Expectations: https://github.com/great-expectations/great_expec

There seems to be a trend where companies are creating tools to monitor data quality... * Great Expectations: https://github.com/great-expectations/great_expectations https://github.com/great-expectations/great_expectations ~2 years old, does data profiling * Data Sentinel: https://engineering.linkedin.com/blog/2020/data-sentinel-automating-data-validation https://engineering.linkedin.com/blog/2020/data-sentinel-aut... It's not open sourced however. * Data Anomaly Detector: https://www.slideshare.net/iblaine/using-airflow-for-tools-development https://www.slideshare.net/iblaine/using-airflow-for-tools-d... Something I built at One Medical that is not open sourced...but this deck has some interesting concepts in it. The approach here is to minimize the effort, lines of code, number of clicks needed, to test your data. * Amundsen: https://github.com/lyft/amundsen https://github.com/lyft/amundsen This is open sourced, and some contributes are building features for data quality analysis. Amundsen seems like it has a bright future.