Table of Contents

Search

  1. Preface
  2. Working with Transformations
  3. Aggregator Transformation
  4. Custom Transformation
  5. Custom Transformation Functions
  6. Data Masking Transformation
  7. Data Masking Examples
  8. Expression Transformation
  9. External Procedure Transformation
  10. Filter Transformation
  11. HTTP Transformation
  12. Identity Resolution Transformation
  13. Java Transformation
  14. Java Transformation API Reference
  15. Java Expressions
  16. Java Transformation Example
  17. Joiner Transformation
  18. Lookup Transformation
  19. Lookup Caches
  20. Dynamic Lookup Cache
  21. Normalizer Transformation
  22. Rank Transformation
  23. Router Transformation
  24. Sequence Generator Transformation
  25. Sorter Transformation
  26. Source Qualifier Transformation
  27. SQL Transformation
  28. Using the SQL Transformation in a Mapping
  29. Stored Procedure Transformation
  30. Transaction Control Transformation
  31. Union Transformation
  32. Unstructured Data Transformation
  33. Update Strategy Transformation
  34. XML Transformations

Transformation Guide

Transformation Guide

Pipeline Normalizer Transformation

Pipeline Normalizer Transformation

When you create a Normalizer transformation in the Transformation Developer, you create a pipeline Normalizer transformation by default. When you create a pipeline Normalizer transformation, you define the columns based on the data the transformation receives from a another type of transformation such as a Source Qualifier transformation. The Designer creates the input and output Normalizer transformation ports from the columns you define.
The following figure shows the Normalizer transformation columns for a transformation that receives four sales columns in each relational source row:
The Normalizer tab in the Edit Transformations dialog box contains the name, level, occurs, datatype, precision, and scale columns. The Normalizer tab also contains the Select transformation and Description fields. The Sales_By_Quarter column occurs four times.
Each source row has a StoreName column and four instances of Sales_By_Quarter.
The source rows might contain the following data:
Dellmark 100 450 650 780 Tonys    666 333 444 555
The pipeline Normalizer transformation has an input port for each instance of a multiple-occurring column.
The following figure shows the ports that the Designer creates from the columns in the Normalizer transformation:
The Normalizer transformation is open and displays the port names and datatypes.
The Normalizer transformation returns one row for each instance of the multiple-occurring column:
Dellmark 100 1 1 Dellmark 450 1 2 Dellmark 650 1 3 Dellmark 780 1 4 Tonys    666 2 1 Tonys    333 2 2 Tonys    444 2 3 Tonys    555 2 4
The Integration Service increments the generated key sequence number each time it processes a source row. The generated key links each quarter sales to the same store. In this example, the generated key for the Dellmark row is 1. The generated key for the Tonys store is 2.
The transformation returns a generated column ID (GCID) for each instance of a multiple-occurring field. The GCID_Sales_by_Quarter value is always 1, 2, 3, or 4 in this example.

0 COMMENTS

We’d like to hear from you!