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1. Preface
2. Introduction to Transformations
3. Transformation Ports
4. Transformation Caches
6. Aggregator Transformation
7. Association Transformation
9. Case Converter Transformation
10. Classifier Transformation
11. Comparison Transformation
12. Consolidation 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. Macro Transformation
30. Match Transformation
31. Match Transformations in Field Analysis
32. Match Transformations in Identity Analysis
33. Normalizer Transformation
34. Merge Transformation
35. Parser Transformation
36. Python Transformation
37. Rank Transformation
39. Relational to Hierarchical Transformation
40. REST Web Service Consumer Transformation
41. Router Transformation
42. Sequence Generator Transformation
43. Sorter Transformation
44. SQL Transformation
45. Standardizer Transformation
46. Union Transformation
47. Update Strategy Transformation
48. Web Service Consumer Transformation
49. Parsing Web Service SOAP Messages
50. Generating Web Service SOAP Messages
51. Weighted Average Transformation
52. Window Transformation
53. Write Transformation
54. Appendix A: Transformation Delimiters

# Data Type Conversion

Multiple expression and aggregation functions might generate data of a data type that is different from the input data.
For example, when you multiply two decimal numbers with a precision of 18 digits, the resulting data type could be a decimal with a precision of 28 digits.
For input data type of Decimal with a precision of 38 digits, the result of certain operations might produce data that may not fit into resultant data type. So the user might get an overflow exception.
The following functions might require a data type conversion to accommodate an increase in the size of the data when compared to the input data types:
• avg
• cume
• divide
• median
• movingavg
• movingsum
• multiply
• Percentile
• Sum
For example, when the input data is of the Integer data type and you use multiplication operation, the resulting data type could be of the bigint data type. Similarly, when the input data type is of the Decimal data type of precision as 18 digits, the result of the multiplication operation might be large and within Decimal data type of the precision as 28 digits.
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