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. Working with Test Data Warehouse
  13. Analyzing Test Data with Data Coverage
  14. Plans and Workflows
  15. Monitor
  16. Reports
  17. ilmcmd
  18. tdwcmd
  19. tdwquery
  20. Data Type Reference
  21. Data Type Reference for Test Data Warehouse
  22. Data Type Reference for Hadoop
  23. Glossary

Data Sets

Data Sets

A data set is a collection of data that consists of one or more tables from a data source. Manage and store multiple versions of data sets in the test data warehouse.
Tables in a data set can be from a single data source or from multiple data sources. A data set generally corresponds to one or more test cases. You can create data sets based on the test cases that you want to run, and store them in the test data warehouse. You can create a data set from TDM when you perform one or more of the following tasks:
  • Create a subset of production data.
  • Create a masked copy of production data.
  • Generate data.
To create a data set, select the test data warehouse as the target when you run a plan. You can add tags and other metadata information to identify the data set. Metadata includes the source table metadata and plan metadata. You can search the test data warehouse based on the metadata properties.
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 warehouse. You can create multiple versions of a data set.
You can create another version of a data set in the following ways:
  • Rerun the plan that you used to create the data set without changing the name of the data set.
  • Create and run a different plan and enter the same data set name.
Different versions of a data set can have similar metadata or 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. This version contains different data and metadata.
Test data on a test environment can get corrupted. You might require the original test data to run additional tests. You can return a test environment to a specific previous state when you reset a specific data set from the test data warehouse to the required connection.
For example, consider a situation where test teams run multiple test cases, or when multiple test teams work on an application. Store the original test data as a data set in the test data warehouse. When one test team completes testing, create another version of the data set with the modified test data. You can then reset the original test data from the test data warehouse to the test environment. You can reset any version of a data set to return the test environment to the required state.
View the list of data sets in the
Data Sets
view.