Table of Contents

Search

  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 Generation Techniques

Data Generation Techniques

You can apply generation techniques based on the target datatype that you configure for a column.
For example, if the column datatype is numeric, you can define generation values that are within a fixed range or values in a sequence. You can select an additional rule to create values that are not valid values within the data set.
The following table describes the generation techniques that you can choose when you define a rule:
Generation Technique
Description
Custom
Standard technique that applies generation rules from a mapplet. The custom mapplet accepts input and uses the logic in the mapplet to generate the output.
Dictionary
Standard technique that imports dictionary values to generate data.
Effective Dates
Ad hoc technique that generates related dates for two columns in the target database.
Expression
Ad hoc technique that accepts expression as the input to generate the output.
Random
Standard technique that generates random strings, values, and dates.
Reference Lookup
Ad hoc technique that generates data from a reference lookup table. You can have multiple column assignments within a reference lookup rule.
Sequence
Standard technique that generates numeric and date values in a sequence.
Set of Values
Standard technique that uses a finite set of values to use in data generation.