Table of Contents

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

Transformations

Transformations

Key masking

Key masking

A column configured for key masking returns deterministic masked data each time the source value and seed value are the same. The Data Masking transformation returns unique values for the column.
When you configure a column for key masking, the Data Masking transformation creates a seed value for the column. You can change the seed value to produce repeatable data between different Data Masking transformations. For example, configure key masking to enforce referential integrity. Use the same seed value to mask a primary key in a table and the foreign key value in another table.
You can configure masking rules that affect the format of data that the Data Masking transformation returns. You can mask numeric, string, and datetime data types with key masking.
When you can configure key masking for datetime values, the Data Masking transformation requires a random number as a seed. You can change the seed to match the seed value for another column to return repeatable datetime values between the columns. The Data Masking transformation can mask dates between 1753 and 2400 with key masking. If the source year is in a leap year, the Data Masking transformation returns a year that is also a leap year. If the source month contains 31 days, the Data Masking transformation returns a month that has 31 days. If the source month is February, the Data Masking transformation returns February. The Data Masking transformation always generates valid dates.
Configure key masking for numeric source data to generate deterministic output. When you configure a column for numeric key masking, you assign a random seed value to the column. When the Data Masking transformation masks the source data, it applies a masking algorithm that requires the seed.
You can configure key masking for strings to generate repeatable output. Configure a mask format to define limitations for each character in the output string. To define a mask format, configure the Source Filter characters and the Target Filter characters. The source filter characters define the source characters to mask. The target filter characters define the characters to mask the source filter characters with.

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