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I am using Great Expectations in my ETL data pipeline for a POC. I have a validation which is failing (as expected), and I have the following data in my validation JSON:

 "unexpected_count": 205,
 "unexpected_percent": 10.25,
 "unexpected_percent_nonmissing": 10.25,
 "unexpected_percent_total": 10.25

Is there a way to identify/segregate these bad records (depicted as unexpected_count)?

Kuwali
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  • 4 months later, and no answers. Does that mean Great Expectations can't do this? I also see a similar thread on their support page without a clear answer: https://discuss.greatexpectations.io/t/how-to-separate-bad-data-records-from-good-data/358 – MattG Jul 06 '22 at 05:04

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