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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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Normalizer Example Definition

Normalizer Example Definition

The source is a flat file that contains store information and quarterly sales data. Define the structure of the source data in the
Normalizer
 view.
The STORE flat file contains the following source data:
StoreID
Store_Name
Store_City
District
Manager
Quarter1
Quarter2
Quarter3
Quarter4
1
BigStore
New York
East
Robert
100
300
500
700
2
SmallStore
Phoenix
West
Radhika
250
450
650
850
Add the flat file to a mapping as a Read transformation, and then create an empty Normalizer transformation. Drag the ports from the Read_STORE data object to the Normalizer transformation to create the Normalizer definition.
The
Normalizer
 view contains one instance of the Store_Name, Store_City, District, and Manager fields. The
Normalizer
 view contains four instances of a field called QUARTER. Merge the QUARTER fields to create one SalesByQuarter field that occurs four times.
The following figure shows the Normalizer definition with merged Quarter fields:
The Normalizer tab in the Properties view shows the Normalizer definition. The STORE field has an Occurs value of one. The QUARTER field has an occurs value of four.
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