Table of Contents

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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Data Subset
  8. Data Masking
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Data Sets
  13. Plans and Workflows
  14. Monitor
  15. Reports
  16. ilmcmd
  17. tdwcmd
  18. tdwquery
  19. Data Type Reference
  20. Data Type Reference for Test Data Warehouse
  21. Data Type Reference for Hadoop
  22. Glossary

Data Set Versions

Data Set Versions

Test data might change when you use the data to run test cases. You can store the changed data as another version of a data set in the test data mart.
Each time you add a data set to the test data mart, you create another version of the data set. A data set version refers to a specific version of a data set in the test data mart. For example, consider a situation where a test team runs tests on different features of an application. Before you use the data, store the data in the test data mart. You create the first version of the data set. To test different features, you add tables and columns to the test data. You can store the updated test data with the same data set name in the test data mart. You create the second version of the data set. You can add versions to the test data mart each time you want to save the test data.
To identify a data set version, add tags to a data set version. You can use tags to perform a keyword search for data set versions. When you create a version of a data set, the version inherits the tags of the previous version. You can edit inherited tags and add additional tags.
To prevent users from editing a data set version or resetting the data set version, you can lock the data set version. For example, if you do not want users to reset a specific data set version to a target, you can lock the data set.
You can create another version of a data set in the following ways:
  • You can rerun the plan that you used to create the previous version of the data set without changing the name of the data set.
  • You can create and run a different plan and enter the same data set name.
Data set versions can have similar metadata or entirely different metadata. For example, you create a data set version DA_APP when you test version 1.0 of an application for a specific customer. You test specific features of the application in this release. You use source data that contains tables relevant to the tests you run. You might add tags to identify the customer. When you test the next version of the application for the customer, you test different features that require different test data. You create and run a different plan with a different data source. Because this is test data for a single application, you enter the same data set name to create another version of the data set. The version retains the tags that identify the customer.