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

Encryption

Encryption

Encryption masking applies encryption algorithms to mask source data.
Mask string data types with encryption masking.
You can choose to preserve the format and length of the source data or the length of the source data. You can also choose to change the format and length of the source data after encryption.
You can choose characters that you do not want to encrypt.
After you encrypt the source data, you can also decrypt it to get back the original data. To decrypt the data, you must create and run a mapping that uses the same encryption technique with the same pass phrase that you used to encrypt the source data. Set the mode to Decryption.
If the source data contains UTF-8 four byte characters, you cannot use encryption to mask the data.
Select one of the following encryption techniques:
Preserve Format and Metadata
Use the Preserve Format and Metadata encryption option to preserve the format and the length of the source data. When you choose to preserve format and metadata, all uppercase characters are replaced with uppercase characters, lowercase characters are replaced with lowercase characters, numbers are replaced with numbers, and special characters are replaced with special characters after encryption. For example, an email address Abc123@xyz.com might become Mpz849#dje!kuw. In this example, if you configure "@" and "." characters as Do Not Encrypt Characters, the email might become Mpz849@dje.kuw.
Preserve Metadata
Use the Preserve Metadata encryption option to preserve the length of the source data. When you choose to preserve metadata, the length of the data remains the same after encryption. For example, a first name Alexender might become jl6#HB91v, where the length remains the same as in the source data.
Change Metadata
Use the Change Metadata encryption option to change the length of the source data after encryption. When you choose to change metadata, the encrypted data does not retain the length and format of the source data. For example, a city name London might become Xuep@8f5, fmch529, or 6ky#ke33h*we.
Before you use the Change Metadata encryption option, you must change the precision of the column you want to apply encryption on in the database.
Use the following formula to calculate the precision and round up the value to the next higher integer:
Required Precision = (1.33*Original Precision)+24
After you change the column precision in the database, you must update the column precision in the mapping. To update the column precision you can either reimport the metadata from the updated database, or manually change the column precision in each transformation in the mapping.

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