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  • Informatica Data Quality 10.5
  • All Products

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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Bad Record Exception Mappings

Bad Record Exception Mappings

When you create a mapping that identifies bad record exceptions, you configure the mapping to write records to one or more database targets based on the quality of the data in the records.
The following figure shows an example Bad Record Exception mapping:
The mapping includes a set of icons that are linked by arrows. An Employees data source object links to a Bad Records mapplet and a Bad Records Exception transformation. The Bad Records mapplet is also an input to the Bad Records Exception transformation. The Bad Records Exception transformation links to data targets for employee records, bad records, and bad record issues.
The mapping contains the following objects:
Data source
An Employees data source that contains the records to analyze for data quality.
Mapplet
The Bad_Records_Mapplet contains transformations that check for and add quality issues and record scores to source records. Rules are transformations that analyze the data and find the quality issues. For example, you can include a Labeler transformation to compare input data to reference tables. Depending on the results, you can configure the Labeler transformation to return quality issues as additional columns in the rows. You can configure a Decision transformation that uses IF, THEN, ELSE statements to examine the data and apply quality issues and record scores to the input data.
Exception transformation
The Exception transformation determines which records to write to the data targets including the bad records table and the issues table.
Good record table
The Exception transformation writes all good-quality records to the target_Employees table.
Bad Record table
The Exception transformation writes all bad-quality records to the target_EmployeeBadRecords table. Bad records require manual review.
Issues table
The Exception transformation writes quality issues to the target_EmployeeBadRecords_ISSUES table. When you view the bad records in the Analyst tool, the user interface links the quality issues to the bad records.
Optionally, the Exception transformation can write rejected records to a rejected records table. You must choose to create a separate output group for rejected records on the
Configuration
 view of the transformation.
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