Comments (3)
So in this comment Neils the maintainer makes the point that
"validation logic needs to be decoupled from more opinionated storage systems, so being able to write your constraint logic in plain Python allows for portability of your data validation layer (i.e. you don't get stuck with a particular set of opinions at the storage layer)"
which I think is the tension we're feeling between inline checks and declarative catalog entries.
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Ah ah I saw the same post and had the exact same feeling.
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I think we should support both, but push users towards the pythonic API the more I think about it
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Related Issues (20)
- Add data validation to terminal outputs HOT 1
- Enable lazy validation at a dataset level
- Add kedro catalog validate command
- Run the pipeline with fake pandera-generated data
- Add a preview of the schema in kedro-viz
- Generate HTML documentation from schema HOT 3
- Temporarily deactivate runtime validation HOT 3
- Enable multiple schema validation
- Upgrade requirements with a valid kedro version HOT 1
- `kedro pandera coverage` HOT 2
- Release v0.1.0 HOT 7
- Remove leftover print statement in `resolve_dataframe_model` HOT 1
- Add kedro~=0.19.0 compatability HOT 2
- fix for kedro >= 0.19.0 HOT 3
- Allow converting the Dataframe according to the defined schema
- AttributeError due to missing `metadata` parameter in datasets
- Validating output data fails on a MemoryDataset HOT 1
- Raising errors for pyspark dataframe validation HOT 4
- Factory datasets are not getting validated HOT 1
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