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. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Column Analysis

Column Analysis

When you configure a Match transformation, you select one or more columns for analysis.
The Match transformation analyzes columns in pairs. When you select a single column for analysis, the transformation creates a temporary copy of the column and compares the source column with the temporary column. When you select two columns for analysis, the transformation compares the values across the two columns that you select. The transformation compares each value in one column with all of the values in the other column. The transformation returns a match score for each pair of values that it analyzes.
You select the columns to analyze when you configure a strategy in the Match transformation. The strategy specifies the columns to analyze and the algorithm to apply to the columns. The algorithm calculates the levels of similarity between each pair of values. The different algorithms in the transformation use different criteria to measure the levels of similarity between the values. You can define multiple strategies in a transformation, and you can and assign different columns to each strategy.

Column Analysis Example

You want to compare the values in a column of surname data. You create a mapping that includes a data source and a Match transformation. You connect the
Surname
port to the Match transformation. The transformation creates a temporary copy of the data on the
Surname
port when the mapping runs.
The following image shows a fragment of the surname data:
The spreadsheet contains two columns of surname data. Column A represents the data on a transformation input port. Column B represents the temporary copy of the data that the transformation generates for match analysis.
The mapping generates a set of match scores that indicate that the following values might be duplicates:
  • Baker, Barker
  • Barker, Parker
  • Smith, Smith
When you review the data, you decide that
Baker, Barker,
and
Parker
are not duplicate values. You decide that
Smith
and
Smith
are duplicate values.

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