Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Merge Transformation
  33. Normalizer Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Write Transformation
  52. Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Uses for a Dynamic Lookup Cache

Uses for a Dynamic Lookup Cache

You can configure a Lookup transformation with a dynamic lookup cache to update the cache based on changes in the lookup source.
You might use a dynamic lookup cache for the following reasons:
Update a master customer table with new and updated customer information.
For example, you can use a Lookup transformation to perform a lookup on the customer table to determine if a customer exists in the target. The cache represents the customer table. The Lookup transformation inserts and update rows in the cache as it passes rows to the target.
Use an exported flat file as a lookup source instead of a relational table.
If the connection to the database is slow, you can export the relational table contents to a flat file and use the file as a lookup source. For example, you might need to use this method if an ODBC connection to a database is slow. You can configure the database table as a relational target in the mapping and pass the lookup cache changes back to the database table.

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