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

Profiling Functionality Support

Profiling Functionality Support

You can connect to the profiling warehouse through JDBC or native connectivity. You can perform specific functionality in profiling based on the profiling warehouse connection.
The following table lists the functionality that you can perform based on the type of profiling warehouse connection that you choose in Data Engineering Quality and Data Engineering Integration:
Functionality
JDBC Connection
Native Connection
Single data object column profile
Supported
Supported
Single data object data domain discovery
Supported
Supported
Single data object profile with primary key discovery
Not supported
Not supported
Single data object profile with functional dependency discovery
Not supported
Not supported
Profile with
Sample first <number> rows
sampling
Supported
Supported
Profile with
Random sample <number> rows
sampling
Supported
Supported
Scorecard metrics and value cost
Not supported
Supported
Enterprise discovery profile with primary key and foreign key discovery
Not supported
Not supported
Enterprise discovery profile with join analysis and overlap discovery
Not supported
Not supported
Drill down on inferred values and value frequencies
Supported
Supported
Export profile results
Supported
Supported
Row count mapping*
Not supported
Supported
*Profiling runs Row Count Mapping when the stats helper fails to report the row count.

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