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Mention & differentiate from similar packages in README #65

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kwinkunks opened this issue Sep 22, 2023 · 0 comments

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kwinkunks commented Sep 22, 2023 •

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Great Expectations - seems big and cumbersome - lesson: stay lean
Evidently - "framework to evaluate, test and monitor ML models in production." - looks nice but quite plugged in to Jupyter, eg lots of plots
ydata-quality - from the same people as profiling (below), and does not look well maintained
Pandas Profiling - generates some alerts eg see below - should test this on my usual datasets

More specialized:

Pandera - "statistical data validation for pandas" (and only pandas) - lesson: support other data formats
pandas_dq - looks quite nice, pands only
Spectacles - continuous integration tool for Looker and LookML (a GCP service?)py
Datafold - time-series and point clouds?
dbt (Data Build Tool) - not sure what this is
Deequ - targeted at Spark/PySpark dataframes only I think

Couple of nice posts etc

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kwinkunks changed the title ~~Mention & diff from similar packages in README~~ Mention & differentiate from similar packages in README

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