Include predefined check functions by default when applying custom checks by metadata #203
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mwojtyczka
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✅ 127/127 passed, 1 skipped, 16m54s total Running from acceptance #533 |
alexott
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Mar 3, 2025
mwojtyczka
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Mar 10, 2025
* Added uniqueness check([#200](#200)). A uniqueness check has been added, which reports an issue for each row containing a duplicate value in a specified column. This resolves issue [154](#154). * Added column expression support for limits in not less and not greater than checks, and updated docs ([#200](#200)). This commit introduces several changes to simplify and enhance data quality checking in PySpark workloads for both streaming and batch data. The naming conventions of rule functions have been unified, and the `is_not_less_than` and `is_not_greater_than` functions now accept column names or expressions as limits. The input parameters for range checks have been unified, and the logic of `is_not_in_range` has been updated to be inclusive of the boundaries. The project's documentation has been improved, with the addition of comprehensive examples, and the contribution guidelines have been clarified. This change includes a breaking change for some of the checks. Users are advised to review and test the changes before implementation to ensure compatibility and avoid any disruptions. Reslves issues: [131](#131), [197](#200), [175](#175), [205](#205) * Include predefined check functions by default when applying custom checks by metadata ([#203](#203)). The data quality engine has been updated to include predefined check functions by default when applying custom checks using metadata in the form of YAML or JSON. This change simplifies the process of defining custom checks, as users no longer need to manually import predefined functions, which were previously required and could be cumbersome. The default behavior now is to import all predefined checks. The `validate_checks` method has been updated to accept a dictionary of custom check functions instead of global variables. This improvement resolves issue [#48](#48).
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mwojtyczka
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Mar 10, 2025
* Added uniqueness check([#200](#200)). A uniqueness check has been added, which reports an issue for each row containing a duplicate value in a specified column. This resolves issue [154](#154). * Added column expression support for limits in not less and not greater than checks, and updated docs ([#200](#200)). This commit introduces several changes to simplify and enhance data quality checking in PySpark workloads for both streaming and batch data. The naming conventions of rule functions have been unified, and the `is_not_less_than` and `is_not_greater_than` functions now accept column names or expressions as limits. The input parameters for range checks have been unified, and the logic of `is_not_in_range` has been updated to be inclusive of the boundaries. The project's documentation has been improved, with the addition of comprehensive examples, and the contribution guidelines have been clarified. This change includes a breaking change for some of the checks. Users are advised to review and test the changes before implementation to ensure compatibility and avoid any disruptions. Reslves issues: [131](#131), [197](#200), [175](#175), [205](#205) * Include predefined check functions by default when applying custom checks by metadata ([#203](#203)). The data quality engine has been updated to include predefined check functions by default when applying custom checks using metadata in the form of YAML or JSON. This change simplifies the process of defining custom checks, as users no longer need to manually import predefined functions, which were previously required and could be cumbersome. The default behavior now is to import all predefined checks. The `validate_checks` method has been updated to accept a dictionary of custom check functions instead of global variables. This improvement resolves issue [#48](#48).
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Resolves #48
Tests