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Releases: databrickslabs/dqx

v0.1.13

27 Feb 13:52
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  • Fixed cli installation and demo (#177). In this release, changes have been made to adjust the dashboard name, ensuring compliance with new API naming rules. The dashboard name now only contains alphanumeric characters, hyphens, or underscores, and the reference section has been split for clarity. In addition, demo for the tool has been updated to work regardless if a path or UC table is provided in the config. Furthermore, documentation has been refactored and udpated to improve clarity. The following issue have been closed: #171 and #198. It may be required to uninstall and install DQX again to redeploy the dashboard.
  • [Feature] Update is_(not)_in_range (#87) to support max/min limits from col (#153). In this release, the is_in_range and is_not_in_range quality rule functions have been updated to support a column as the minimum or maximum limit, in addition to a literal value. This change is accomplished through the introduction of optional min_limit_col_expr and max_limit_col_expr arguments, allowing users to specify a column expression as the minimum or maximum limit. Extensive testing, including unit tests and integration tests, has been conducted to ensure the correct behavior of the new functionality. These enhancements offer increased flexibility when defining quality rules, catering to a broader range of use cases and scenarios.

Contributors: @karthik-ballullaya-db, @mwojtyczka

v0.1.12

13 Feb 18:38
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  • Fixed installation process for Serverless (#150). This commit removes the pyspark dependency from the librar to avoid spark version conflicts in Serverless and future DBR versions. CLI has been updated to install pyspark for local command execution.
  • Updated demos and documentation (#169). In this release, the quality checks in the demos have been updated to better showcase the capabilities of DQX. Documentation has been updated in various places for increase clarity. Additional contributing guides have been added.

Contributors: @mwojtyczka

v0.1.11

12 Feb 12:14
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What's Changed

  • Provided option to customize reporting column names (#127). In this release, the DQEngine library has been enhanced to allow for customizable reporting column names. A new constructor has been added to DQEngine, which accepts an optional ExtraParams object for extra configurations. A new Enum class, DefaultColumnNames, has been added to represent the columns used for error and warning reporting. New tests have been added to verify the application of checks with custom column naming. These changes aim to improve the customizability, flexibility, and user experience of DQEngine by providing more control over the reporting columns and resolving issue #46. Contributors @hrfmartins @mwojtyczka
  • Fixed parsing error when loading checks from a file (#165). In this release, we have addressed a parsing error that occurred when loading checks (data quality rules) from a file, fixing issue #162. The specific issue being resolved is a SQL expression parsing error. The changes include refactoring tests to eliminate code duplication and improve maintainability, as well as updating method and variable names to use filepath instead of "path". Additionally, new unit and integration tests have been added and manually tested to ensure the correct functionality of the updated code. Contributors @mwojtyczka
  • Removed usage of try_cast spark function from the checks to make sure DQX can be run on more runtimes (#163). In this release, we have refactored the code to remove the usage of the try_cast Spark function and replace it with cast and isNull checks to improve code compatibility, particularly for runtimes where try_cast is not available. The affected functionality includes null and empty column checks, checking if a column value is in a list, and checking if a column value is a valid date or timestamp. We have added unit and integration tests to ensure functionality is working as intended. Contributors @mwojtyczka
  • Added filter to rules so that you can make conditional checks (#141). The filter serves as a condition that data must meet to be evaluated by the check function. The filters restrict the evaluation of checks to only apply to rows that meet the specified conditions. This feature enhances the flexibility and customizability of data quality checks in the DQEngine. Contributors @pierre-monnet @mwojtyczka

Full Changelog: v0.1.8...v0.1.11

v0.1.10

04 Feb 11:40
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Full Changelog: v0.1.8...v0.1.10

v0.1.9

24 Jan 18:36
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Full Changelog: v0.1.8...v0.1.9

v0.1.8

23 Jan 16:48
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Full Changelog: v0.1.7...v0.1.8

v0.1.7

21 Jan 17:47
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Full Changelog: v0.1.6...v0.1.7

v0.1.6

17 Jan 11:48
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Full Changelog: v0.1.5...v0.1.6

v0.1.5

15 Jan 17:14
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Full Changelog: v0.1.4...v0.1.5

v0.1.4

10 Jan 19:23
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Release v0.1.4 (#79)

## Changes
Updated release process