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

Transformations

Transformations

Field mappings

Field mappings

The Union transformation can merge data from multiple source pipelines. The sources can have the same set of fields, have some matching fields, or use parameterized field mappings.
When you work with field mappings in a Union transformation, note the following:
  • You must use input groups where the fields have the identical name, type, precision, and scale.
  • You can edit, remove, or manually add some of the output fields.
  • As part of the field mapping, you choose an input group and specify the parameter from the input group.
  • You can use parameters for fields in all input groups.
  • You can parameterize the field mapping or map by field name for each input group. At run time, the task adds an exact copy of the fields from the input group as output fields.
If you want
Data Integration
to automatically link fields with the same name and you also want to manually map fields, select the
Manual
option and click
Automap
.
You can map fields in the following ways:
  • Exact Field Name.
    Data Integration
    matches fields of the same name.
  • Smart Map.
    Data Integration
    matches fields with similar names. For example, if you have an incoming field
    Cust_Name
    and a target field
    Customer_Name
    ,
    Data Integration
    automatically links the
    Cust_Name
    field with the
    Customer_Name
    field.
You can use both Exact Field Name and Smart Map in the same field mapping. For example, use Exact Field Name to match fields with the same name and then use Smart Map to map fields with similar names.
You can undo all automapped field mappings by clicking
Automap
Undo Automap
. To unmap a single field, select the field to unmap and click
Actions
Unmap
.
Data Integration
highlights newly mapped fields. For example, when you use Exact Field Name,
Data Integration
highlights the mapped fields. If you then use Smart Map,
Data Integration
only highlights the fields mapped using Smart Map.

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