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Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Back Next

Data Masking Transformation on the Spark Engine

Data Masking Transformation on the Spark Engine

The Data Masking transformation is supported with the following restrictions.
Mapping validation fails in the following situations:
  • The transformation is configured for repeatable expression masking.
  • The transformation is configured for unique repeatable substitution masking.
You can use the following masking techniques on this engine:

    Credit Card

    Email

    Expression

    IP Address

    Key

    Phone

    Random

    SIN

    SSN

    Tokenization

    URL

    Random Substitution

    Repeatable Substitution

    Dependent with Random Substitution

    Dependent with Repeatable Substitution

To optimize performance of the Data Masking transformation, configure the following Spark engine configuration properties in the Hadoop connection:
spark.executor.cores
Indicates the number of cores that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.cores=1
spark.executor.instances
Indicates the number of instances that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.instances=1
spark.executor.memory
Indicates the amount of memory that each executor process uses to run tasklets on the Spark engine.
Set to:
spark.executor.memory=3G
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