Table of Contents


  1. Preface
  2. Data integration tasks
  3. Mapping tasks
  4. Dynamic mapping tasks
  5. Synchronization tasks
  6. Data transfer tasks
  7. Replication tasks
  8. Masking tasks
  9. Masking rules
  10. PowerCenter tasks



Masking tasks


tasks to mask the sensitive fields in source data with realistic test data for nonproduction environments. You can choose to create a subset of the sensitive source data that reconciles object relationships.
When you configure a
task, choose the source and target and then select a masking rule for each field in the source you want to mask. You can also use inplace masking to mask the data in the same system from which the
task reads the data.
A data masking rule is a type of masking that you can apply to a selected field. The type of masking rule that you apply depends on the type of the field that you need to mask. You can select built-in rules for masking fields such as Social Security numbers, credit card numbers, phone numbers, and dates. You can apply substitution values for fields such as names, cities, countries, or positions. You can mask fields with random values or with repeatable values.
For example, you might need to test a Human Resources application. You need realistic employee data to test with. You can mask the fields in an Employee table to create the test data.
You can apply masking parameters to some data masking rules. Masking parameters are options that you can apply to customize the rules.
If the source and target locations are different, you can create a subset of the source data. Define data subset criteria to selectively process source data. For example, you can use a data subset to create a small environment for testing and development. You can define the type of data that you want to include in the subset database. The data subset retains foreign key relationships from the source data.


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