Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings
  4. Sources
  5. Targets
  6. Transformations
  7. Data Preview
  8. Cluster Workflows
  9. Profiles
  10. Monitoring
  11. Hierarchical Data Processing
  12. Hierarchical Data Processing Configuration
  13. Hierarchical Data Processing with Schema Changes
  14. Intelligent Structure Models
  15. Stateful Computing
  16. Appendix A: Connections
  17. Appendix B: Data Type Reference
  18. Appendix C: Function Reference

Hadoop Integration

Hadoop Integration

Big Data Management can connect to clusters that run different Hadoop distributions. Hadoop is an open-source software framework that enables distributed processing of large data sets across clusters of machines. You might also need to use third-party software clients to set up and manage your Hadoop cluster.
Big Data Management can connect to the supported data source in the Hadoop environment, such as HDFS, HBase, or Hive, and push job processing to the Hadoop cluster. To enable high performance access to files across the cluster, you can connect to an HDFS source. You can also connect to a Hive source, which is a data warehouse that connects to HDFS.
It can also connect to NoSQL databases such as HBase, which is a database comprising key-value pairs on Hadoop that performs operations in real-time. The Data Integration Service can push mapping jobs to the Spark or Blaze engine, and it can push profile jobs to the Blaze engine in the Hadoop environment.
Big Data Management supports more than one version of some Hadoop distributions. By default, the cluster configuration wizard populates the latest supported version.


Updated July 10, 2020