Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Monitoring Mappings in the Hadoop Environment
  7. Mappings in the Native Environment
  8. Profiles
  9. Native Environment Optimization
  10. Data Type Reference
  11. Function Reference
  12. Parameter Reference

Validation Environments

Validation Environments

The properties in the
Validation Environments
indicate whether the Developer tool validates the mapping definition for the native execution environment or the Hadoop execution environment.
You can configure the following properties for the
Validation Environments
:
Native
Default environment. The Data Integration Service runs the mapping in a native environment.
Hadoop
Run the mapping in the Hadoop environment. The Data Integration Service pushes the transformation logic to the Hadoop cluster through a Hive connection. The Hadoop cluster processes the data. Select the Hive on MapReduce engine, the Blaze engine, or the Spark engine to process the mapping. The Hadoop connection must contain the configuration properties for each engine that you choose. If you choose Hive on MapReduce engine, you can also select the Hive version. Select a version number from the list or assign a parameter to the Hive version. The parameter must be a string that contains a version from the Hive version list. If you use the Blaze engine, you cannot clear the
Hive on MapReduce
engine.
You can use a mapping parameter to indicate the execution environment. When you select the execution environment, click
Assign Parameter
. Configure a string parameter. Set the default value to Native or Hive.
When you validate the mapping, validation occurs for each engine that you choose in the
Validation Environments
. The validation log might contain validation errors specific to each engine. If the mapping is valid for at least one mapping, the mapping is valid. The errors for the other engines appear in the validation log as warnings. If the mapping is valid for multiple Hadoop engines, you can view the execution plan to determine which engine will run the job. You can view the execution plan in the
Data Viewer
view.
The following image shows validation errors for the Blaze engine, the Spark engine, and the Hive on MapReduce engine:


Updated November 09, 2018