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Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Address Validator Transformation
  4. Aggregator Transformation
  5. Association Transformation
  6. Bad Record Exception Transformation
  7. Case Converter Transformation
  8. Classifier Transformation
  9. Cleanse transformation
  10. Comparison Transformation
  11. Custom Transformation
  12. Custom Transformation Functions
  13. Consolidation Transformation
  14. Data Masking Transformation
  15. Data Masking Examples
  16. Decision Transformation
  17. Duplicate Record Exception Transformation
  18. Dynamic Lookup Cache
  19. Expression Transformation
  20. External Procedure Transformation
  21. Filter Transformation
  22. HTTP Transformation
  23. Identity Resolution Transformation
  24. Java Transformation
  25. Java Transformation API Reference
  26. Java Expressions
  27. Java Transformation Example
  28. Joiner Transformation
  29. Key Generator Transformation
  30. Labeler Transformation
  31. Lookup Transformation
  32. Lookup Caches
  33. Match Transformation
  34. Match Transformations in Field Analysis
  35. Match Transformations in Identity Analysis
  36. Merge Transformation
  37. Normalizer Transformation
  38. Parser Transformation
  39. Rank Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. Source Qualifier Transformation
  44. SQL Transformation
  45. Using the SQL Transformation in a Mapping
  46. Stored Procedure Transformation
  47. Standardizer Transformation
  48. Transaction Control Transformation
  49. Union Transformation
  50. Unstructured Data Transformation
  51. Update Strategy Transformation
  52. Weighted Average Transformation
  53. XML Transformations

Transformation Guide

Transformation Guide

Ports Tab

Ports Tab

When you define a Normalizer transformation, you configure the columns in the Normalizer tab. The Designer creates the ports. You can view the Normalizer ports and attributes on the Ports tab.
Pipeline and VSAM Normalizer transformations represent multiple-occurring source columns differently. A VSAM Normalizer transformation has one input port for a multiple-occurring column. A pipeline Normalizer transformation has multiple input ports for a multiple-occurring column.
The Normalizer transformation has one output port for each single-occurring input port. When a source column is multiple-occurring, the pipeline and VSAM Normalizer transformations have one output port for the column. The transformation returns a row for each source column occurrence.
The Normalizer transformation has a generated column ID (GCID) port for each multiple-occurring column. The generated column ID is an index for the instance of the multiple-occurring data. For example, if a column occurs four times in a source record, the Normalizer returns a value of 1, 2, 3, or 4 in the generated column ID based on which instance of the multiple-occurring data occurs in the row.
The naming convention for the Normalizer generated column ID is GCID_<
occuring_field_name
>.
The Normalizer transformation has at least one generated key port. The Integration Service increments the generated key sequence number each time it processes a source row.
The following figure shows the Normalizer transformation Ports tab:
The Ports tab in the Edit Transformations dialog box contains the port name, datatype, precision, scale, input, and output columns. The tab also contains the Select transformation, Transformation type, Default value, and Description fields. nrm_pipeline_normalizer is entered in the select transformation field. The transformation type is Normalizer (Reusable).
In this example, Sales_By_Quarter is multiple-occurring in the source. The Normalizer transformation has one output port for Sales_By_Quarter. It returns four rows for each source row. Generated key start value is 1.
You can change the ports on a pipeline Normalizer transformation by editing the columns on the Normalizer tab. To change a VSAM Normalizer transformation, you need to change the COBOL source and recreate the transformation.

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