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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. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

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Union Transformation example

Union Transformation example

You have demographic data about employees from two flat file sources and you want to merge that data.
You receive the following data in a .txt file: 
employee_ID,first_name,last_name,location,email,phone
1211,John,Davis,Redwood City,jdavis@infa2.com,555-555-4444
0233,Miles,Simone,Barcelona,msimone@infa2.com,555-555-6666
1045,Billie,Coltrane,Philadelphia,bcoltrane@infa2.com,555-555-7777
0987,Django,Holiday,Paris,dholiday@infa3.com,444-444-4444
1199,Nina,Reinhardt,New York,nreinhardt@infa3.com,444-555-5555
A second file contains the following data: 
ID,first,last,dept,e-mail,phone
0456,Joni,Smith,Marketing,j_smith@infa4.com,333-333-3333
1325,David,Mitchell,R&D,dmitchell@infa4.com,222-222-2222
1101,David,Harry,R&D,dharry@infa5.com,777-777-7777
0623,Debbie,Byrne,HR,dbyrne@infa5.com,888-888-8888
0777,Patti,Bowie,Sales,pbowie@infa5.com,999-999-9999
You want to merge those records into a single dataset in MySQL with the following columns:
  • id
  • last
  • first
  • email
  • phone
Remember that the data to be merged with a Union transformation must have the same data type, precision, and scale.
To merge the files with a Union transformation, complete the following steps:
  1. Ensure that the source files reside in a location accessible to your Secure Agent.
  2. Define a connection to access the .csv files.
  3. Create a mapping in the Mapping Designer.
  4. Add two Source transformations to the mapping to connect to data in the .csv files.
  5. Add a Union transformation and connect the Source transformations to it.
  6. In the Union transformation Properties, perform the following steps for each input group:
    1. In the Field Rules section, click the group you want to configure.
    2. (Optional) For the incoming fields, select the fields you want to merge in the output.
      The following image shows the selected fields in the first input group:
      All the named fields are included from the first input group except for the "location" field.
      The following image shows the selected fields in the second input group:
      All the named fields are included from the second input group except for the "dept" field.
      If you do not specify a rule to exclude fields, at run time, the task ignores any fields that you do not map to the output fields.
    3. Edit the Output field names in the Union transformation, to correspond to the field names that you want in the target:
      The Output Fields tab of the Union transformation shows the output fields that will be included in the data flow. In this image, there are five output fields: email, first, ID, last, and phone.
      You can also select fields, change metadata, add other fields, or convert the field types, for example, from integer to number.
  7. In the Field Mapping of the Union transformation, ensure that the fields are correctly mapped for each input group:
    On the Field Mapping tab of the Union transformation, input group "Input1" is selected, and the field map options are set to "Manual." Each output field has been mapped to a corresponding incoming field.
  8. Add a Target transformation to the mapping.
  9. Connect the Union transformation to the Target transformation.
  10. In the Target transformation field mapping, select automatic field mapping:
    On the Field Mapping tab of the Target transformation, the field map opitions are set to "Automatic." Therefore, the incoming fields will be automatically mapped to the target fields.
When complete, the mapping appears similar to the following image:
The completed mapping contains two pipelines: In the first pipeline, source "s_ees_jz" is connected to input group "Input1" of the Union transformation, and the Union transformation is connected to the target. In the second pipeline, source "s_ees_rk" is connected to input group "Input2" of the Union transformation, and the Union transformation is connected to the target.
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