Cloning in Datamaker

You as test engineer need adhoc or regular copies of customer data. You need only the most pertinent subset of the data, because testing the full, redundant data set wastes time. When copying data, you encounter typical data integrity issues: The additional environment planning to accommodate for data copying interferes with development and production work schedules. Also, all personally identifiable data must be masked in copied data, and it must be masked consistently.
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You as test engineer need adhoc or regular copies of customer data. You need only the most pertinent subset of the data, because testing the full, redundant data set wastes time. When copying data, you encounter typical data integrity issues: The additional environment planning to accommodate for data copying interferes with development and production work schedules. Also, all personally identifiable data must be masked in copied data, and it must be masked consistently.
For example: if you replace country names in one table with random letters, and you replace phone prefixes in another table with random numbers, then your phone number validation tests fail unnecessarily, and the tests become meaningless. Therefore, you need to ensure that complex test data is copied and masked in a coordinated manner. 
Cloning in Datamaker retains core characteristics of the data, and maintains cross-application integrity:
  • Datamaker extracts data from multiple systems.
  • Datamaker masks data in transit.
  • Datamaker assigns new keys every time when data is loaded.
  • Testers can request only the data subset that suits a specific problem (TDM Portal).
  • Testers receive test data adhoc, after a short time (TDM Portal).
  • Testers can receive consistent copies of the same data and can run tests in parallel. (TDM Portal)
  • Testers can pass on test data among each other in a controlled way (TDM Portal)
 
 
 
Video: Data Cloning in Datamaker -- Concepts
 
 

 
 
 
 
Video: Data Cloning in Datamaker -- How To
 
 

 
Store a Custom Cross Reference ID List in the Repository
When cloning and subsetting in Datamaker, you have the option to store a custom generated cross reference list in the repository.
  1. Click the 
    LoV Options
     button in the toolbar.
  2. Enable 
    Store Xref Values in Test Data Repository
     to activate this functionality. 
For example, you want to publish a table in a datapool to clone data between a source and target. You use an xref function in the table's data definition:
  1. Read an identifying ID from a column in the source, and generate a new ID to be used in the target. 
  2. Choose a list name in which to store the mapped values.
    The xref maps source and target ID values, and stores them in the given cross reference list.
  3. Publish the table. 
  4. CA Datamaker publishes and inserts the row that you defined in the xref list.
  5. Click 
    Tools
    View and Authorize Jobs
    .
    The 
    View Publish Logs
     window opens.
  6. Click the 
    Stored Values
     tab for the last job and verify that the xref mapping that you created contains the values from target and source table.
  7. Use the xlookup function to retrieve the mapping of the two values in a later expression.
 Xrefpersist performs the same basic function as xref, but additionally stores the specified old and new values in the repository. CA TDM portal stores only the table-column combinations that use xrefpersist in the repository. In CA Datamaker, using @xrefpersist once causes all values used in both @xref and @xrefpersist to be stored to the repository. 
 
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