Table of Contents

Search

  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

Bad Record Exception Output Record Types

Bad Record Exception Output Record Types

The Bad Record Exception examines input record scores to determine record quality. It returns records to different output groups
The Exception transformation identifies the following types of records based on each record score:
Good records
Records with scores greater than or equal to the upper threshold. Good records are valid and do not require review. For example, if configure the upper threshold as 90, any record with a 90 or higher does not need review.
Bad records
Records with scores less than the upper threshold and scores greater than or equal to the lower threshold. Bad records are the exceptions that you need to review in the Analyst tool. For example, when the lower threshold is 40, any record with a score from 40 to 90 needs manual review.
Rejected records
Records with scores less than the lower threshold. Rejected records are not valid. By default, the Exception transformation drops rejected records from the data flow. For this example, any record with a score 40 or less is a rejected record.
If the quality issues fields are NULL, the record is not an exception. When any quality issue contains text or it contains an empty string, the record is an exception. Verify that a quality issue port contains null values when a field has no error. If the quality issue ports contain blanks instead of null values then the Exception transformation flags every record as an exception. When a user needs to correct the issues in the Analyst tool, the user cannot filter the exceptions by data quality issue.
When a record has a score less than zero or greater than 100, the row is not valid. The Data Integration Service logs an error message that the row is not valid and it skips processing the record.
If you do not connect a record score as input to the Exception transformation, the transformation writes all records that contain quality issues to the bad records table.
When you include the Bad Record Exception transformation in a Mapping task, you can configure a Human task in the same workflow to include a manual review of the exceptions. The Human task starts when a Mapping task ends in the workflow. The Human task requires users to access the Analyst tool to resolve the quality issues. A user can update the data and change the quality status of each record in the bad records table.

0 COMMENTS

We’d like to hear from you!