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I understand that you will like to know the definition of valid and invalid in the context of Databrew.
The validity of a column in Databrew is based on the column type. For instance , if we have a column called HIRE_DATE with column type date:
- Valid in this context refers to the percentage of values with a valid date value
- Invalid refers to the percentage of values with an invalid date value
To replace the invalid values with select 'Invalid' and transformation and replace with desired options presented
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This does not explain why DataBrew decided my entire column should be null, nor does it really answer the question in any way.