Table of Contents

Search

  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

Groups in duplicate analysis

Groups in duplicate analysis

A mapping that reads a Deduplication transformation may take a long time to run because of the number of data comparisons that the transformation must perform. The number of comparisons relates to the number of input fields that the transformation must analyze.
The following table shows the number of calculations that a mapping performs for different numbers of data values on a single field:
Number of data values
Number of comparisons
10,000
50 million
100,000
5,000 million
1 million
500,000 million
To reduce the time that the mapping takes to run, the transformation can organize the input records into groups. A group is a set of records that contain identical values on a field that you specify. When you perform duplicate analysis on grouped data, the transformation analyzes the field data on the records within each group. The transformation does not compare the records in one group with the records in another group. The groups reduce the overall number of comparisons that the transformation must perform without any meaningful loss of accuracy in the mapping analysis.
Consider the following rules and guidelines when you organize data into groups:
  • The field on which you group the data is the
    GroupKey field
    . Find the field on the
    Field Mapping
    tab in the transformation. A group key field must contain a predictable range of expected duplicate values, such as a city name or a state name in an address data set.
    The presence of duplicate values in the group key field does not mean that the respective input records must also be duplicates.
  • Do not specify a group key field that the asset requires to identify the identity information in the input data.
  • Groups do not reorder the position of the records in the mapping data set.