Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Data Masking transformation example

Data Masking transformation example

You need realistic data for testing in a nonproduction environment. You have production data that includes sensitive data columns. You cannot use the data without the risk of compromising sensitive data. Use the Data Masking transformation to mask sensitive data before you use the data in a test environment.
The production data includes a table Personnel_Information with the following data:
First Name
Surname
DOB
Address
State
Uma
Hilel
03/12/1985
24, Atkins Avenue
CA
John
Sen
07/15/1963
49, Wheeler Road
MN
Keiko
Burns
11/24/1989
13, Draker Drive
PA
Isadora
Buckley
08/16/1990
20, Fountain Center
CA
In the Mapping Designer, add
Personnel
as a source transformation for the table Personnel_Information. Add a target transformation Personnel_test.
Add the Data Masking transformation to the mapping canvas and connect it to the data flow.
You need to mask the Surname, DOB, and the State columns to ensure sensitive data is masked. You can use the Substitution Last Name masking technique to mask the Surname column. This masking technique replaces data in the column with data from the dictionary file on surnames. You can use the Random Date masking technique to mask the DOB column. Use the Substitution State masking technique to mask the State column. This masking technique replaces data in the column with data from the dictionary file on U.S. state names.
The Mapping Designer shows that masking techniques are assigned to the incoming fields in the Data Masking transformation object. The Substitution last name technique is assigned to the Surname field, the Random technique is assigned to the DOB field, and the Substitution state technique is assigned to the State field.
When the mapping run completes successfully, you can view the masked data in the Personnel_test output table :
First Name
Surname
DOB
Address
State
Uma
Acothley
05/12/1985
24, Atkins Avenue
Michigan
John
Mcgovern
04/15/1963
49, Wheeler Road
Oklahoma
Keiko
Garsia
03/24/1990
13, Draker Drive
Montana
Isadora
Sonnier
01/16/1991
20, Fountain Center
South Carolina
You can now use the masked data in the table Personnel_test in the test environment.

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