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

Transformations

Transformations

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Inheriting data sources from the parent

Inheriting data sources from the parent

When you configure the data source for an array or struct field, you can populate the children of the field either with the incoming data or by inheriting the parent's data sources.
When you use the incoming data, the incoming data is used to populate the children of the array or struct.
When you inherit the parent's data sources, the data transformed into the parent output field populates the children of the array or struct. This preserves the data transformations (for example, joins and filters), in the parent fields. You can apply filters to the field to further filter the data, but you can't configure data sources, joins, group by fields, or order by fields.
For example, you are reading data from a relational table of customer records in which the customer ID is unique. The incoming data contains the following records: 
CustID,Name,Street,City,State,ZIP
00234,Ravindra Singh,123 6th St. Apt. 5A,Boston,MA,02134
14416,Melissa Clark,11 Winding Way,Watch Hill,RI,02891
You want to write the customer address fields to a struct.
The following image shows the incoming and output fields:
The image shows the Hierarchy Processor tab of the Hierarchy Processor transformation. The Incoming Fields panel contains one input group with the following string fields: CustID, Name, Street, City, State, and ZIP. The Output Fields panel contains one output group with the following fields: CustID (string), Name (string), and Address (struct). The Address struct contains the following string fields: Street, City, State, and ZIP.
In the Output Fields panel, set the data source for the Output group to
Input
 and the data source for the Address struct to
Inherit parent's data sources (Output)
. When you run the mapping, the Hierarchy Processor transformation creates one record for each occurrence of CustID in the input data and populates the struct with the address data that corresponds to the customer ID in the output: 
{
	"CustID":"00234",
	"Name":"Ravindra Singh",
	"Address":{
		"Street":"123 6th St. Apt. 5A",
		"City":"Boston",
		"State":"MA",
		"ZIP":"02134"
	}
}
{
	"CustID":"14416",
	"Name":"Melissa Clark",
	"Address":{
		"Street":"11 Winding Way",
		"City":"Watch Hill",
		"State":"RI",
		"ZIP":"02891"
	}
}
If you set the data source for the Address struct to
Input
, then you must also configure the following filter condition on the struct to get the same output:
:fld.{Input.CustID} = :fld.{Output.CustID} AND :fld.{Input.Name} = :fld.{Output.Name}
. For more information about configuring filter conditions, see Configure filter conditions.
When the output field is an array that inherits its parent's data, the Hierarchy Processor transformation creates an array with one element.
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