Table of Contents


  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



Router transformation examples

Router transformation examples

You can use a Router transformation to complete the following tasks:
  • Group data by different country attributes and route each group to different target tables based on conditions that test for the region.
  • Group inventory items by different price categories and route each group to different target tables based on conditions that test for low, medium, and high prices.

Example 1: Route data to different targets by region.

Your source includes data for customers in different regions. You want to configure a marketing campaign with variants for customers in the North America region, the Europe, Middle East, and Africa region, and the Asia Pacific region. All other customers see the default ad campaign. Use a Router transformation to route the data to four different Target transformations.
The following figure shows a mapping with a Router transformation that filters data based on these conditions:
The Router transformation routes customer data to different targets based on region, either NA, EMEA, or APAC. The transformation routes data for other regions to the default target.
Create three output groups and specify the group filter conditions on the
Output Groups
tab as shown in the following table:
Group Name
region = ‘NA’
region = ‘EMEA’
region = ‘APAC’
The default group includes data for all customers that are not in the NA, EMEA, or APAC region.

Example 2: Route some rows to multiple output groups.

The Router transformation passes data through all output groups that meet the filter condition. In the following example, the conditions test for a price threshold, but the filter conditions for the two output groups overlap:
Group Name
item_price > 100
item_price > 500
When the Router transformation processes an input row with item_price=510, it routes the row to both output groups.
If you want to pass the data through a single output group, define the filter conditions so that they do not overlap. For example, you might change the filter condition for PriceGroup1 to item_price <= 500.


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