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Table of Contents

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

Transformation Guide

Transformation Guide

Run the Data Viewer

Run the Data Viewer

Run the Data Viewer to review the results of the match analysis. By default, the Data Viewer shows all the output ports on the Match transformation. When you run the mapping, you update the data target with the data from the output ports.
The following image shows the output data in the Data Viewer:
The Data Viewer shows the output data from the Match transformation output ports, including the row I.D. ports.
The Data Viewer verifies that the customer account data includes one or more duplicate records.
Consider the following data in the Data Viewer:
  • The transformation determines that the records for Augusta Chan and Carmen Chan might contain the same information because they contain the same surname and address data. When you review the records, you decide that the records are unique in the data set. However, you notice that the records share a common customer ID value. Because the customer ID column is a primary key in the data set, you contact the New York office. The New York office resolves the error.
  • The transformation determines that the records for Keith Anderson might contain the same information. When you review the records, you verify that the two records represent the same account. However, you notice that the records have different customer ID values. Because a customer account must have a single ID value, you contact the San Antonio office. The San Antonio office resolves the error.

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