Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Overview of Mapping Transformations in the Hadoop Environment

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
  • Blaze
  • Spark*
  • Hive
Aggregator
  • Blaze
  • Spark
  • Hive
Case Converter
  • Blaze
  • Spark*
  • Hive
Classifier
  • Blaze
  • Spark*
  • Hive
Comparison
  • Blaze
  • Spark*
  • Hive
Consolidation
  • Blaze
  • Spark*
  • Hive
Data Masking
  • Blaze
  • Spark
  • Hive
Data Processor
  • Blaze
  • Hive
Decision
  • Blaze
  • Spark*
  • Hive
Expression
  • Blaze
  • Spark
  • Hive
Filter
  • Blaze
  • Spark
  • Hive
Java
  • Blaze
  • Spark
  • Hive
Joiner
  • Blaze
  • Spark
  • Hive
Key Generator
  • Blaze
  • Spark*
  • Hive
Labeler
  • Blaze
  • Spark*
  • Hive
Lookup
  • Blaze
  • Spark
  • Hive
Match
  • Blaze
  • Spark*
  • Hive
Merge
  • Blaze
  • Spark*
  • Hive
Normalizer
  • Blaze
  • Spark
  • Hive
Parser
  • Blaze
  • Spark*
  • Hive
Python
  • Spark
Rank
  • Blaze
  • Spark
  • Hive
Router
  • Blaze
  • Spark
  • Hive
Sequence Generator
  • Blaze
  • Spark*
Sorter
  • Blaze
  • Spark
  • Hive
Standardizer
  • Blaze
  • Spark*
  • Hive
Union
  • Blaze
  • Spark
  • Hive
Update Strategy
  • Blaze
  • Spark*
  • Hive
Weighted Average
  • Blaze
  • Spark*
  • Hive
*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
.


Updated October 23, 2019