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  1. Preface
  2. Data Integration performance tuning overview
  3. Optimizing targets
  4. Optimizing sources
  5. Optimizing mappings
  6. Optimizing mapping tasks
  7. Optimizing advanced clusters
  8. Optimizing system performance

Data Integration Performance Tuning

Data Integration Performance Tuning

Data Integration performance tuning overview

Data Integration
performance tuning overview

The goal of performance tuning is to optimize mapping performance by eliminating performance bottlenecks.
To tune the performance of a mapping, first identify a performance bottleneck, eliminate it, and then identify the next performance bottleneck until you are satisfied with the performance.
Determining the best way to improve performance is an iterative process. Change one variable at a time, and time the mapping both before and after the change. If the mapping performance doesn't improve, you might want to return to the original configuration.
Performance issues can occur for any of the following reasons:
  • Best practices not followed
  • Target bottlenecks
  • Source bottlenecks
  • Transformation bottlenecks
  • System bottlenecks

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