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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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Cluster Output

Cluster Output

The Target_CustomerConsolidate table receives records from the Cluster Output group. The Cluster Output group returns records that might be duplicate records. The records in the Target_CustomerConsolidate table need manual review in the Analyst tool.
The following image shows some of the records and the fields in the Target_CustomerConsolidate table:
The record contains the following fields:
Row_Identifier
A number that uniquely identifies each row in the table.
Sequential Cluster ID
A sequential identifier for each cluster to review in a Human task. The Duplicate Record Exception transformation adds the sequential cluster ID to records in the Cluster Data output group.
Cluster ID
A unique identifier for a cluster. The Match transformation assigns a cluster ID to all the output records. Duplicate records and possible duplicate records share a cluster ID. A unique record receives a cluster ID, but the record does not share the ID number with any other record.
Score
Match score that indicates the degree of similarity between a record and another record in the cluster. Records that require manual review have match scores less than .95 and greater than .80.
Is Master
Indicates whether the record is the preferred record in the cluster.
WorkflowID
The WorkflowID is DummyWorkflowID because the transformation is not in a workflow.
Record Fields
The other fields in the record contain the customer source data.
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