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  • Informatica Data Quality 10.4.0
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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

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Standard Output Table Records

Standard Output Table Records

The Write_GoodMatches_Customer target table receive rows from the Standard Output group. The table receives unique records and it receives records that are duplicates. These records do not require manual review.
The following figure shows the Standard Output records that the Exception transformation returns:
The record contains the following fields:
Score
Match score that indicates the degree of similarity between a record and another record in the cluster. Records with a match score of one are duplicate records that do not need review. A cluster in which any record has a match score below the lower threshold is not a duplicate cluster.
Row_Identifier
A row number that uniquely identifies each row in the table. For this example, the row identifier contains the customer ID.
Cluster ID
A unique identifier for a cluster. Each record in a cluster receives the same cluster ID. The first four records in the sample output data are unique. Each record has a unique cluster ID. Rows five to nine belong to cluster five. Each record in this cluster is a duplicate record because of similarities in the address fields.
Source Data Fields
The Standard Output table group also receives all the source data fields.
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