Table of Contents

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

Transformations

Transformations

Mask rule parameter example

Mask rule parameter example

You can create a mask rule parameter to use a single mapping to mask data in multiple sources or to avoid updating a mapping each time you add more columns to the source data that you want to mask.
See the following examples for more information:
  • You create a mapping with a specific source and target and add the masking techniques as well. Over time, you add on more columns to the source table and want to mask those columns as well. The mapping then needs to be edited before it can be run again. To avoid having to edit the mapping, you can use a mask rule parameter when you configure it. You can then select columns to mask at runtime. If you added any columns after the mapping was created, you can select them at runtime without updating the mapping.
  • You might have source data in multiple databases. Rather than creating multiple mappings for each source and target combination, you might want to create a single mapping to mask your source data in any of the databases and load them to different target databases. To run a mapping with different source and target connections each time, you can use parameters for the source and target connections and objects. You then need to choose different masking techniques depending on the source connection. To do this, add a mask rule parameter to the data masking transformation in your mapping. You can then choose the source connection and object, the masking rules to apply to each column, and the target connection and object at runtime. You can run the same mapping multiple times after selecting different values for these fields depending on which source and target you want to use.
Perform the following high-level tasks to run a mapping task with source, target, and mask rule parameters:
  1. Create a mapping with parameters.
  2. Run the mapping. You can run a mapping directly or from a mapping task.

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