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  • Informatica Data Quality 10.4.1
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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. 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

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Connect the Output Ports

Connect the Output Ports

Connect the Match transformation output ports to the data target in the mapping. Select the ports that contain the record data that you want to write to the data target.
The transformation includes a series of preset ports for clustered data. Select the preset ports that indicate duplicate status of the records and identify the data source that stores each record.
The following ports contain data that you can use to find duplicate records and determine the source or the records:
  • The
    ClusterSize
     port indicates the number of records in a cluster. If a record belongs to a cluster with a cluster size greater than 1, the transformation considers the record to be a duplicate of another record.
  • The
    ClusterID
     port identifies the cluster that a record belongs to. Use the ClusterID data to find the records that are duplicates of the current record.
  • The
    PersistenceStatus
     port uses a code value to describe the relationship between the index data from the mapping source and the index data in the data store.
  • The
    PersistenceStatusDesc
     port returns a text description of the values on the PersistenceStatus port code.
You can use other ports to review the relationships between the cluster records. The link port values and driver port values indicate the extent of the similarity between the records in each cluster.
In the current example, you connect all the ports to the data target. To view the output data on the ports, run the Data Viewer.
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