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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. 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.

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