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With the pointblank package it’s really easy to methodically validate your data whether in the form of data frames or as database tables. On top of the validation toolset, the package gives you the means to provide and keep up-to-date with the information that defines your tables. For table validation, the agent object works with a large collection of simple (yet powerful!) validation functions. We can enable much more sophisticated validation checks by using custom expressions, segmenting the data, and by selective mutations of the target table. The suite of validation functions ensures that everything just works no matter whether your table is a data frame or a database table. Sometimes, we want to maintain table information and update it when the table goes through changes. For that, we can use an informant object plus associated functions to help define the metadata entries and present it as a data dictionary.
Features
- Data validation can be carried out in Data Quality Reporting workflow
- The pointblank package is designed to be both straightforward yet powerful
- Local data frames don’t take very long to validate extensively and all validation checks on remote tables are done entirely in-database
- The Pipeline Data Validation workflow uses the same collection of validation functions but without need of an agent
- Using pointblank in an R Markdown workflow is enabled by default once the pointblank library is loaded
- Table information can be synthesized in an information management workflow
Project Samples
