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

When to Use a Parser Transformation

When to Use a Parser Transformation

Use the Parser transformation when the data fields in a column contain more than one type of information and you want to move the field values to new columns. The Parser transformation lets you create new column for each type of information in a data set.
The following examples describe some types of structural change you can perform with a Parser transformation.
Create new columns for contact data
You can create a data structure that parses name data from a single column into multiple columns. For example, you can create columns for salutations, first names, middle names, and surnames.
You configure the transformation with a probabilistic model that represents the structures of the person names on the input port. You use a sample of the input port data to define the model.
You create a token parsing strategy that applies the probabilistic model to the input port and writes the name values to new columns. The transformation writes the name values to the new columns based on the position of each value in the input string and the type of name that the value represents.
You can also use a pattern-based parsing strategy to parse contact data. When you configure a pattern-based parsing strategy, you define the patterns that represents the structures of the names on the input port.
Create address columns
You can create a data structure that parses a single column of address data into multiple columns that describe a deliverable address.
Configure the transformation with reference tables that contain recognizable address elements, such as ZIP Codes, state names, and city names. Create a token parsing strategy that writes each address element to a new port.
You cannot use a reference table to parse street address data from an input string, because street name and number data is too general to be captured in a reference table. However, you can use the Overflow port to capture this data. When you have parsed all city, state, and ZIP data from an address, the remaining data contains street information.
For example, use a token parsing strategy to split the following address into address elements:
123 MAIN ST NW STE 12 ANYTOWN NY 12345
The parsing strategy can write the address elements to the following columns:
Column Name
Data
Overflow
123 MAIN ST NW STE 12
City
ANYTOWN
State
NY
ZIP
12345
Create product data columns
You can create a data structure that parses a single column of product data into multiple columns that describe the product inventory details.
Configure the transformation with token sets that contain inventory elements, such as dimension, color, and weight. Create a token parsing strategy that writes each inventory element to a new port
For example, use a token parsing strategy to split the following paint description into separate inventory elements:
500ML Red Matt Exterior
The parsing strategy can write the address elements to the following columns:
Column Name
Data
Size
500ML
Color
Red
Style
Matt
Exterior
Y

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