Table of Contents


  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



Field rule configuration examples

Field rule configuration examples

The following examples show how you can configure field rules in a transformation.

Changing incoming field names

You need to create a mapping to gather revenue data from multiple sales locations. You learn that multiple fields from the upstream transformation have the same names as fields in a source transformation. To avoid field name conflicts, you change the field names for all incoming fields so that the source is distinguishable throughout the mapping. To change the names of incoming fields, you create a field rule to rename all fields with the SalesForce_ prefix.
To increase performance, you want to ensure that the data set only includes required data. You determine that information regarding transaction dates is not required, so you remove the data fields from the mapping. You create a rule to exclude fields that are not required. To exclude date fields, you create a rule to exclude fields with a date/time data type.
You review the order of the rules. You want the rule to rename the fields to run after the rule to exclude the date/time fields. You move the rule to remove date/time fields so that it appears before the renaming rule.

Removing patterns in field names

You can use parentheses to group different patterns and use a reference to replace the pattern. In a regular expression, parentheses group the patterns to be matched. You can use a
reference to select a group matched in the input string.
You might need to change patterns in fields between transformations. If fields in an upstream transformation contain the suffix
, you can remove the suffix from the field names in the current transformation. Use the following expression to remove the
. The
part of the expression matches all the field name characters,
matches the suffix, and
references the input string specified by the matched
field name characters.
The following expression shows another example:
In a matched string of
, for example, the matches are
, and
. The
value is referenced by
is the first group in the match.
After you apply this field rule to rename the fields, Data Integration appends a suffix, beginning with 1, to multiple fields with the same name. This convention removes duplicate field names.


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