feat: add DQDL support via EvaluateDataQuality (#205) - #288
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Wrap com.amazon.deequ.dqdl.EvaluateDataQuality so Python users can evaluate DQDL rulesets, including DataFreshness for checking how recent data is. Adds pydeequ.dqdl.EvaluateDataQuality with process() and processRows(), supporting additionalDataSources and pandas output, plus tests and docs.
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Superseded by #289. |
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Issue #, if available: Closes #205
Description of changes:
Deequ already supports data freshness as a DQDL rule (awslabs/deequ#661, in every release since 2.0.14, including the 2.0.21 jar PyDeequ pins). PyDeequ doesn't expose DQDL, so Python users can't use it. This PR adds a thin wrapper around
com.amazon.deequ.dqdl.EvaluateDataQuality.Changes
pydeequ/dqdl.py:EvaluateDataQuality.processandprocessRows, matching the Scala API. SupportsadditionalDataSources={"alias": df}for rules likeRowCountMatch,pandas=Trueoutput, and pandas input (same asVerificationSuite.onData).tests/test_dqdl.py: output schema, passing and failingDataFreshnessrules (including the compliance metric),days/minutesunits,additionalDataSources, pandas output, row-level outcomes, and input validation.README.mdanddocs/source/pydeequ.rst: usage section and API docs.No changes to existing APIs or the Deequ dependency.
Notes
DataFreshnessdoesn't have row-level support in Deequ, soprocessRowslists it underDataQualityRulesSkip. The README says this.dqdldependency.Testing
pytest tests/test_dqdl.py tests/test_verification.py: 15 passed locally (Spark 3.5.9, Python 3.11, Java 17, Deequ 2.0.21-spark-3.5). black and flake8 pass on the new files.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.