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  • Informatica Data Quality 10.4.1
  • 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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Issues Table

Issues Table

The Issues table contains one row for each row in the Bad Records table. Each row contains the issues that the data quality analysis found for the source record.
The following figure shows the columns in the Issues table:
The Issues table contains the following columns:
Workflow_ID
Identifies the workflow that created the record. The workflow contains the Exception transformation Mapping task and the Human task to review the issue.
Row_Identifier
Identifies the record row in the database table. The row identifier identifies which row in the Bad Records table corresponds to the row in the Issues table.
Issue Field Name
The field name is the name of the field that might quality issues. When the field contains an error the column value is the quality issue text. In the figure above, the ADDR2 field name contains the
invalid_locality
 quality issue.
DQAPriority
The issue priority. When multiple issues occur for the same field, the issue with the highest priority appears in the Issue Field Name.
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