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

Row-Based Strategies

Row-Based Strategies

A row-based strategy analyzes rows in the record group and selects one row. The Consolidation transformation uses the port values from that row to create a consolidated record. The default strategy is "most data."
Choose one of the following row-based strategies:
Most data
Selects the row with the highest character count. If the highest character count is shared by two or more rows, the strategy returns the last qualifying value.
Most filled
Selects the row with the highest number of non-blank columns. If the highest number of non-blank columns is shared by two or more rows, the strategy returns the last qualifying value.
Modal exact
Selects the row with the highest count of the most frequent non-blank values. For example, consider a row that has three ports that contain the most frequent values in the record group. The count of the most frequent values for that row is "3."
If the highest count of the most frequent non-blank values is shared by two or more rows, the strategy returns the last qualifying value.

Row-Based Strategy Example

The following table displays a sample record group. The last column describes the reasons why specific row-based strategies select different rows in this record group.
Product ID
First Name
Last Name
ZIP Code
Strategy Selection
2106
Bartholomew
28516
The Most Data strategy selects this row because the row contains more characters than the other rows.
2236
Bart
Smith
28579
The Most Filled strategy selects this row because the row has more non-blank columns than the other rows.
2236
<Blank>
Smith
28516
The Modal Exact strategy selects this row because the row contains the highest count of the most frequent values.

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