|author||‘Zhao <firstname.lastname@example.org>||Thu Oct 10 23:13:27 2019 +0800|
|committer||William Guo <email@example.com>||Thu Oct 10 23:13:27 2019 +0800|
[GRIFFIN-289] New feature for griffin COMPLETENESS dq type As describing in GRIFFIN-289, add two new ways to check 'incompleteness' record: regular expression and enumeration. Add 'error.confs' in dq json file. Each json object in 'error.confs' list means one column configuration. If do not have 'error.confs', using old 'incompleteness' process, which is compatible for existing json file. Add ut for the new json format. Author: ‘Zhao <firstname.lastname@example.org> Author: Zhao Li <email@example.com> Closes #538 from LittleZhao/griffin-289.
The data quality (DQ) is a key criteria for many data consumers like IoT, machine learning etc., however, there is no standard agreement on how to determine “good” data. Apache Griffin is a model-driven data quality service platform where you can examine your data on-demand. It provides a standard process to define data quality measures, executions and reports, allowing those examinations across multiple data systems. When you don't trust your data, or concern that poorly controlled data can negatively impact critical decision, you can utilize Apache Griffin to ensure data quality.
You can try running Griffin in docker following the docker guide.
Follow Apache Griffin Development Environment Build Guide to set up development environment.
If you want to contribute codes to Griffin, please follow Apache Griffin Development Code Style Config Guide to keep consistent code style.
If you want to deploy Griffin in your local environment, please follow Apache Griffin Deployment Guide.
For more information about Griffin, please visit our website at: griffin home page.
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