Table of Contents


  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. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter Reference

Parameters Overview

Parameters Overview

A mapping parameter represents a constant value that you can change between mapping runs. Use parameters to change the values of connections, file directories, expression components, port lists, port links, and task properties. You can use system parameters or user-defined parameters.
System parameters are built-in parameters for a Data Integration Service. System parameters define the directories where the Data Integration Service stores log files, cache files, reject files, source files, target files, and temporary files. An administrator defines the system parameter default values for a Data Integration Service in the Administrator tool.
User-defined parameters are parameters that you define in transformations, mappings, or workflows.
Create user-defined parameters to rerun a mapping with different connection, flat file, cache file, temporary file, expression, ports, or reference table values.
You can override parameter values using a parameter set or a parameter file. A parameter set is a repository object that contains mapping parameter values. A parameter file is an XML file that contains parameter values. When you run the mapping with a parameter set or a parameter file, the Data Integration Service uses the parameter values defined in the parameter set or parameter file instead of the default parameter values you configured in the transformation, mapping, or workflow.
You can use the following parameters to represent additional properties in the Hadoop environment:
Parameters for sources and targets
You can use parameters to represent additional properties for the following big data sources and targets:
  • Complex file
  • Flat file
  • HBase
  • HDFS
  • Hive
Parameters for the Hadoop connection and run-time environment
You can set the Hive version, run-time environment, and Hadoop connection with a parameter.
For more information about mapping parameters, see the
Informatica Developer Mapping Guide

Updated December 13, 2018