Table of Contents

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  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

Processing Data on a Grid

Processing Data on a Grid

You can run an Integration Service on a grid to increase the processing bandwidth. When you enable grid, the Integration Service runs a service process on each available node of the grid to increase performance and scalability.
Large data sets may require additional bandwidth to process large amounts of data. For example, when you run a Model repository profile on a large data set, the Data Integration Service grid splits the profile into multiple mappings and runs the mappings simultaneously on different nodes in the grid.
When you run mappings on a grid, the Data Integration Service distributes the mappings to multiple DTM processes on nodes in the grid. When you run a profile on a grid, the Data Integration Service splits the profile into multiple mappings and distributes the mappings to multiple DTM processes on nodes in the grid. You can optimize the grid to increase performance and scalability of the Data Integration Service.
To optimize the grid, add notes to the grid to increase processing bandwidth of the Data Integration Service.
For more information about the Data Integration Service grid, see the
Informatica Administrator Guide
.


Updated September 28, 2020