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  • Informatica Data Quality 10.4.0
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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. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

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Store Values Across Rows

Store Values Across Rows

You can configure variables in transformations to store data from source rows. You can use the variables in transformation expressions.
For example, a source file contains the following rows: 
California
California
California
Hawaii    
Hawaii    
New Mexico
New Mexico
New Mexico
Each row contains a state. You need to count the number of rows and return the row count for each state: 
California,3
Hawaii    ,2
New Mexico,3
You can configure an Aggregator transformation to group the source rows by state and count the number of rows in each group. Configure a variable in the Aggregator transformation to store the row count. Define another variable to store the state name from the previous row.
The Aggregator transformation has the following ports:
Port
Port Type
Expression
Description
State
Pass-through 
n/a
The name of a state. The source rows are grouped by the state name. The Aggregator transformation returns one row for each state.
State_Count
Variable 
IIF (PREVIOUS_STATE = STATE, STATE_COUNT +1, 1)
The row count for the current State. When the value of the current State column is the same as the Previous_State column, the Integration Service increments State_Count. Otherwise, it resets the State_Count to 1.
Previous_State
Variable
State
The value of the State column in the previous row. When the Integration Service processes a row, it moves the State value to Previous_State.
State_Counter
Output 
State_Count
The number of rows the Aggregator transformation processed for a state. The Integration Service returns State_Counter once for each state.
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