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. Normalizer Transformation
  33. Merge 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. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Normal Join

Normal Join

With a normal join, the Data Integration Service discards all rows of data from the master and detail source that do not match, based on the condition.
For example, you have two sources of data for auto parts called PARTS_SIZE and PARTS_COLOR.
The PARTS_SIZE data source is the master source and contains the following data:
PART_ID1
DESCRIPTION
SIZE
1
Seat Cover
Large
2
Ash Tray
Small
3
Floor Mat
Medium
The PARTS_COLOR data source is the detail source and contains the following data:
PART_ID2
DESCRIPTION
COLOR
1
Seat Cover
Blue
3
Floor Mat
Black
4
Fuzzy Dice
Yellow
To join the two tables by matching the PART_IDs in both sources, you set the condition as follows:
PART_ID1 = PART_ID2
When you join these tables with a normal join, the result set includes the following data:
PART_ID
DESCRIPTION
SIZE
COLOR
1
Seat Cover
Large
Blue
3
Floor Mat
Medium
Black
The following example shows the equivalent SQL statement:
SELECT * FROM PARTS_SIZE, PARTS_COLOR WHERE PARTS_SIZE.PART_ID1 = PARTS_COLOR.PART_ID2

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