Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Deduplicate transformation
  9. Expression transformation
  10. Filter transformation
  11. Hierarchy Builder transformation
  12. Hierarchy Parser transformation
  13. Hierarchy Processor transformation
  14. Input transformation
  15. Java transformation
  16. Java transformation API reference
  17. Joiner transformation
  18. Labeler transformation
  19. Lookup transformation
  20. Mapplet transformation
  21. Normalizer transformation
  22. Output transformation
  23. Parse transformation
  24. Python transformation
  25. Rank transformation
  26. Router transformation
  27. Rule Specification transformation
  28. Sequence Generator transformation
  29. Sorter transformation
  30. SQL transformation
  31. Structure Parser transformation
  32. Transaction Control transformation
  33. Union transformation
  34. Velocity transformation
  35. Verifier transformation
  36. Web Services transformation

Transformations

Transformations

Deduplicate transformation

Deduplicate transformation

The Deduplicate transformation adds a deduplicate asset that you created in
Data Quality
to a mapping.
Use a Deduplicate transformation to analyze the levels of duplication in a data set and optionally to consolidate sets of duplicate records into a single, preferred record. Deduplicate transformations analyze the
identity
information in the records. An identity is a group of data values in a record that identify a person or an organization.
Deduplication and consolidation are useful operations in the following types of data project:
  • Customer Relationship Management. For example, a store designs a mail campaign and must check the customer database for duplicate customer records.
  • Regulatory compliance initiatives. For example, a business operates under government or industry regulations that insist all data systems are free of duplicate records.
  • Financial risk management. For example, a bank may want to search for relationships between account holders.
  • Any project that must identify or eliminate records that store duplicate identity information.
The transformation is not certified for serverless runtime execution.