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

Dynamic cache and target synchronization

Dynamic cache and target synchronization

Configure downstream transformations to ensure that the dynamic lookup cache and target are synchronized.
When you use a dynamic lookup cache, the
mapping
task writes to the lookup cache before it writes to the target table. The lookup cache and target table can become unsynchronized if the task does not write the data to the target. For example, the target database might reject the data.
Consider the following guidelines to keep the lookup cache synchronized with the lookup table:
  • Use the Router transformation to pass rows to the cached target when the NewLookupRow value equals one or two.
  • Use the Router transformation to drop rows when the NewLookupRow value equals zero. Or, output the rows to a different target.