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

Rank Index

Rank Index

The Developer tool creates a RANKINDEX port for each Rank transformation. The Data Integration Service uses the Rank Index port to store the ranking position for each row in a group.
For example, you might create a Rank transformation to identify the 50 highest paid employees in the company. You identify the SALARY column as the input/output port used to measure the ranks, and configure the transformation to filter out all rows except the top 50.
After the Rank transformation identifies all rows that belong to a top or bottom rank, it then assigns rank index values. In the case of the top 50 employees, measured by salary, the highest paid employee receives a rank index of 1. The next highest-paid employee receives a rank index of 2, and so on. When measuring a bottom rank, such as the 10 lowest priced products in the inventory, the Rank transformation assigns a rank index from lowest to highest. Therefore, the least expensive item would receive a rank index of 1.
If two rank values match, they receive the same value in the rank index and the transformation skips the next value. For example, if you want to see the top five retail stores in the country and two stores have the same sales, the return data might look similar to the following:
RANKINDEX
SALES
STORE
1
10000
Orange
1
10000
Brea
3
90000
Los Angeles
4
80000
Ventura
The RANKINDEX is an output port only. You can pass the rank index to another transformation in the mapping or directly to a target.

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