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. Transformation Delimiters

Transformations in the Native and Non-native Environments

Transformations in the Native and Non-native Environments

Mappings that run in the non-native environment can return different results than mappings that run in the native environment.
Consider the following processing differences:
  • The non-native environment uses distributed processing and processes data on different nodes. Each node does not have access to the data that is being processed on other nodes. As a result, the runtime engine might not be able to determine the order in which the data originated. So, when you run a mapping in a non-native environment and you run the same mapping in the native environment, both mappings return correct results, but the results might not be identical.
  • Each of the run-time engines in the non-native environment can process mapping logic differently. In the non-native environment, Informatica transformations might be fully supported, supported with restrictions, or not supported. Similarly, in the native environment, some Informatica transformations and transformation behavior might not be supported.
The following table lists transformations and support for different engines in a non-native environment:
Transformation
Supported Engines
Transformations not listed in this table are not supported in a non-native environment.
Address Validator
  • Blaze
  • Spark
Aggregator
  • Blaze
  • Spark*
  • Databricks Spark
Case Converter
  • Blaze
  • Spark
Classifier
  • Blaze
  • Spark
Comparison
  • Blaze
  • Spark
Consolidation
  • Blaze
  • Spark
Data Masking
  • Blaze
  • Spark*
Data Processor
  • Blaze
  • Spark**
Decision
  • Blaze
  • Spark
Expression
  • Blaze
  • Spark*
  • Databricks Spark
Filter
  • Blaze
  • Spark*
  • Databricks Spark
Java
  • Blaze
  • Spark*
Joiner
  • Blaze
  • Spark*
  • Databricks Spark
Key Generator
  • Blaze
  • Spark
Labeler
  • Blaze
  • Spark
Lookup
  • Blaze
  • Spark*
  • Databricks Spark
Match
  • Blaze
  • Spark
Merge
  • Blaze
  • Spark
Normalizer
  • Blaze
  • Spark*
  • Databricks Spark
Parser
  • Blaze
  • Spark
Python
  • Spark*
  • Databricks Spark
Rank
  • Blaze
  • Spark*
  • Databricks Spark
Router
  • Blaze
  • Spark*
  • Databricks Spark
Sequence Generator
  • Blaze
  • Spark
Sorter
  • Blaze
  • Spark*
  • Databricks Spark
Standardizer
  • Blaze
  • Spark
Union
  • Blaze
  • Spark*
  • Databricks Spark
Update Strategy
  • Blaze
  • Spark
  • Databricks Spark
Weighted Average
  • Blaze
  • Spark
Window
  • Spark***
* Supported for both batch and streaming mappings.
** Supported with restrictions in batch mappings. Not supported in streaming mappings. For information about the Data Processor transformation support on the Spark engine, see the KB article.
*** Supported for streaming mappings only. For more information, see the
Data Engineering Streaming User Guide
.


Updated June 25, 2020