Table of Contents


  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

Map Data Type

Map Data Type

A map data type represents an unordered collection of key-value pair elements. A map element is a key and value pair that maps one thing to another. To pass, generate, or process map data, assign map data type to ports.
The key must be of a primitive data type. The value can be of a primitive or complex data type. A map data type with values of a complex data type is a nested map. A nested map can contain up to three levels of nesting of map data type.
The transformation language includes subscript operator to access map elements. It also includes functions to generate and process map data.


map <primitive_type -> data_type>
The following table describes the arguments for this data type:
Name of the map column or port.
Data type of the key in a map element.
The key must be of a primitive data type.
Data type of the value in a map element.
The value can be of a primitive or complex data type.

Map Example

The following map column represents map data with an integer key and a string value to map customer ids with customer names:
custid_name <integer -> string>
The following example shows data values for the custid_name column:
<26745 -> 'John Baer'>
<56743 -> 'Bobbi Apperley'>
<32879 -> 'Linda Bender'>