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

Cluster Workflows Overview

Cluster Workflows Overview

You can use a workflow to create a cluster that runs Mapping and other tasks on a cloud platform cluster.
A cluster workflow contains a Create Cluster task that you configure with information about the cluster to create. The cluster workflow uses other elements that enable communication between the Data Integration Service and the cloud platform, such as a cloud provisioning configuration and a Hadoop connection.
If you want to create an ephemeral cluster, you can include a Delete Cluster task. An ephemeral cluster is a cloud platform cluster that you create and use for running mappings and other tasks, then terminate when tasks are complete to save cloud platform resources.
You can use a cluster workflow with the Amazon EMR or Microsoft Azure HDInsight cloud platforms.
To create a cluster on Cloudera Altus, you create a workflow with Command tasks that perform the tasks that a cluster workflow automates. For more information about creating a cluster on Cloudera Altus, see the article "Implementing Informatica Big Data Management with Ephemeral Clusters on a Cloudera Altus Cluster" on the Informatica Network.


Updated October 23, 2019