Table of Contents


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

Native Environment Optimization

Native Environment Optimization

You can optimize the native environment to increase performance. To increase performance, you can configure the Data Integration Service to run on a grid and to use multiple partitions to process data. You can also enable high availability to ensure that the domain can continue running despite temporary network, hardware, or service failures.
You can run profiles, sessions, and workflows on a grid to increase the processing bandwidth. A grid is an alias assigned to a group of nodes that run profiles, sessions, and workflows. When you enable grid, the Data Integration Service runs a service process on each available node of the grid to increase performance and scalability.
You can also run mapping with partitioning to increase performance. When you run a partitioned session or a partitioned mapping, the Data Integration Service performs the extract, transformation, and load for each partition in parallel.
You can configure high availability for the domain. High availability eliminates a single point of failure in a domain and provides minimal service interruption in the event of failure.


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