Overview of Mapping Transformations in the Hadoop Environment
Due to the differences between native environment and Hadoop environment, only certain transformations are valid or are valid with restrictions in the Hadoop environment. Some functions, expressions, data types, and variable fields are not valid in the Hadoop environment.
Consider the following processing differences that can affect whether transformations and transformation behavior are valid or are valid with restrictions in the Hadoop environment:
- •Hadoop 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 Hadoop execution engine might not be able to determine the order in which the data originated.
- •Each of the run-time engines in the Hadoop environment can process mapping logic differently.
The following table lists transformations and support for different engines in a Hadoop environment:
Transformation | Supported Engines |
|---|
Transformations not listed in this table are not supported in the Hadoop environment. |
Address Validator | |
Aggregator | |
Case Converter | |
Classifier | |
Comparison | |
Consolidation | |
Data Masking | |
Data Processor | |
Decision | |
Expression | |
Filter | |
Java | |
Joiner | |
Key Generator | |
Labeler | |
Lookup | |
Match | |
Merge | |
Normalizer | |
Parser | |
Python | |
Rank | |
Router | |
Sequence Generator | |
Sorter | |
Standardizer | |
Union | |
Update Strategy | |
Weighted Average | |
*Not supported for Big Data Streaming on the Spark engine. For more information about Big Data Streaming transformations, see the Informatica Big Data Streaming User Guide. |