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

Process Streamed Data in Real Time

Process Streamed Data in Real Time

You can process streamed data in real time. To process streams of data in real time and uncover insights in time to meet your business needs, use Informatica Big Data Streaming.
Create Streaming mappings to collect the streamed data, build the business logic for the data, and push the logic to a Spark engine for processing. The Spark engine uses Spark Streaming to process data. The Spark engine reads the data, divides the data into micro batches and publishes it.
For more information, see the
Informatica Big Data Streaming User Guide
.


Updated October 23, 2019