Table of Contents

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