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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Creating a Data Subset
  8. Performing a Data Masking Operation
  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. Appendix A: Data Type Reference
  21. Appendix B: Data Type Reference for Test Data Warehouse
  22. Appendix C: Data Type Reference for Hadoop
  23. Appendix D: Glossary

Advanced Generation

Advanced Generation

An advanced generation rule is a combination of generation techniques that generates test data in target columns based on the configuration of one or more than one input port, variable port, and output port.
You can generate test data for string, numeric, and date data types. In an advanced rule, create input columns, variable columns, and output columns based on the test data requirements. Configure a generation rule for a variable column, and add an expression for the output column.
The following image shows the advanced generation rule parameters that you configure:
The New Generation Rule dialog box shows the input, variable, and output columns of an advanced rule. The var variable column is selected and the general properties and generation rule set parameters are displayed.

Advanced Generation Example

You want to generate email IDs for the employees in EMAIL_ID column in the EMPLOYEE table. Create an input port in_FirstName of string data type. Create a variable port var_Domain of string data type. Configure the Generation Rule Set technique for the variable port. Configure two rules to generate yahoo and gmail domain names. Create an output port out_Email to concatenate the input name and the variable domain with the following expression:
CONCAT(CONCAT(in_FirstName,'@'),var_Domain)
Assign the rule to the EMAIL_ID column, and generate and run the workflow to produce the output email IDs.
The following table shows a sample output:
First_Name
EMAIL_ID
Richmond
Richmond@gmail.com
Debora
Debora@gmail.com
Shaniah
Shaniah@yahoo.com
Lena
Lena@yahoo.com

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