Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Using Group Filter Conditions

Using Group Filter Conditions

You can test data based on one or more group filter conditions. You create group filter conditions on the
Groups
tab using the Expression Editor.
You can enter any expression that returns a single value. You can also specify a constant for the condition. A group filter condition returns TRUE or FALSE for each row that passes through the transformation, based on whether a row satisfies the specified condition. Zero (0) is the equivalent of FALSE. Any non-zero value is the equivalent of TRUE. You can use a single numeric port as a filter condition. The Data Integration Service passes the rows of data that evaluate to TRUE to each transformation or target that is associated with each user-defined group.
You cannot use a single dynamic port to return a boolean value.
For example, you have customers from nine countries, and you want to perform different calculations on the data from three countries. You can use a Router transformation in a mapping to filter this data to three different Expression transformations.
You can use parameters as elements in the group filter condition. You can use system parameters or user-defined parameters. You can create parameters from the Expression Editor and add them to the expression.
The default group does not have a group filter condition. However, you can create an Expression transformation to perform a calculation based on the data from the other six countries.
The following figure shows a mapping with a Router transformation that filters data based on multiple conditions:
The mapping contains a source, a Router transformation, and four targets. Each target corresponds to a filter condition.
To perform multiple calculations based on the data from three different countries, create three user-defined groups and specify three group filter conditions on the
Groups
tab.
The following figure shows group filter conditions that filter customer data:
The Groups tab displays the following groups: Default, France, Japan, and USA. The France, Japan, and USA groups have a corresponding filter condition.
The following table shows group filter conditions that filter customer data:
Group Name
Group Filter Condition
France
customer_name=‘France’=TRUE
Japan
customer_name=‘Japan’=TRUE
USA
customer_name=‘USA’=TRUE
In the mapping, the Data Integration Service passes the rows of data that evaluate to TRUE to each transformation or target associated with each user-defined group, such as Japan, France, and USA. The Data Integration Service passes the row to the default group if all of the conditions evaluate to FALSE. The Data Integration Service then passes the data of the other six countries to the transformation or target that is associated with the default group. If you want the Data Integration Service to drop all rows in the default group, do not connect it to a transformation or a target in a mapping.
The Router transformation passes data through each group that meets the condition. If data meets three output group conditions, the Router transformation passes the data through three output groups.
For example, you configure the following group conditions in a Router transformation:
Group Name
Group Filter Condition
Output Group 1
employee_salary > 1000
Output Group 2
employee_salary > 2000
When the Router transformation processes an input row data with employee_salary=3000, it routes the data through output groups 1 and 2.

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