feat: add has_no_aggr_outliers stateless rolling-window sigma outlier check#1118
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Thanks for the contribution @vpottam-nvidia — this is a nicely scoped, stateless counterpart to the existing is_aggr_* checks and it's great to see it paired with both unit and integration tests.
A couple of housekeeping items:
- Please sign your commits — DQX requires signed commits per the First Contribution guide. You'll need to enable
git config --global commit.gpgsign true(or equivalent) and force-push the re-signed history. - Please remove the CHANGELOG.md entry — the changelog is generated automatically during release from PR titles/labels, so manual entries create merge conflicts and get regenerated anyway.
- Please address the inline review comments
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Thanks for submitting! Requested a few changes.
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- Implements has_no_aggr_outliers (dataset-level check): rolling-window sigma outlier detection for time-series aggregates using pure PySpark. Replaces the earlier is_aggr_not_anomalous name per reviewer request. - Validates time_column is date/timestamp type (raises InvalidParameterError instead of cryptic Spark error at query time). - Normalizes group_by column names in alias via get_column_name_or_alias(normalize=True). - Adds 15 integration tests and full unit test coverage. - Adds has_no_aggr_outliers to all_dataset_checks.yaml (exercises apply_checks_by_metadata end-to-end per reviewer request). - Adds test_benchmark_has_no_aggr_outliers perf test. - Updates quality_checks.mdx reference documentation.
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Co-authored-by: Marcin Wojtyczka <marcin.wojtyczka@databricks.com>
Co-authored-by: Marcin Wojtyczka <marcin.wojtyczka@databricks.com>
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Adds
has_no_aggr_outliers, a new dataset-level quality check that detects outliers in time-series aggregates using a stateless rolling-window sigma method.Tests
Documentation and Demos