Most developers and testers use existing production data to develop and test their new products and functionality. Creating test data manually is often far too complex and time consuming and test data generated by synthetic test data solutions are far from realistic and representative. The time that it cost to configure all the generators for large enterprise data models with thousands of columns is not worth the effort.
But how can we improve the provisioning and quality of our test data in the modern software development age. In this training you will learn how to create small subsets of production data, that you can use for testing and development. Working with subsets of production can reduce your storage footprint and save a lot of time and money managing all the test data environments.
You will learn
- How to classify a data model
- How to choose a start table and start filter
- How to auto classify a data model
- How to validate your classification
- Maintaining technical and functional consistency
- Executing subsets
- And a lot more...
DATPROF Subset offers no data browsing capabilities, so we will use Oracle SQL Developer to browse the output of the data.