Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Aggregator Transformation
  4. Custom Transformation
  5. Custom Transformation Functions
  6. Data Masking Transformation
  7. Data Masking Examples
  8. Expression Transformation
  9. External Procedure Transformation
  10. Filter Transformation
  11. HTTP Transformation
  12. Identity Resolution Transformation
  13. Java Transformation
  14. Java Transformation API Reference
  15. Java Expressions
  16. Java Transformation Example
  17. Joiner Transformation
  18. Lookup Transformation
  19. Lookup Caches
  20. Dynamic Lookup Cache
  21. Normalizer Transformation
  22. Rank Transformation
  23. Router Transformation
  24. Sequence Generator Transformation
  25. Sorter Transformation
  26. Source Qualifier Transformation
  27. SQL Transformation
  28. Using the SQL Transformation in a Mapping
  29. Stored Procedure Transformation
  30. Transaction Control Transformation
  31. Union Transformation
  32. Unstructured Data Transformation
  33. Update Strategy Transformation
  34. XML Transformations

Transformation Guide

Transformation Guide

Aggregator Transformation Overview

Aggregator Transformation Overview

The Aggregator transformation performs aggregate calculations, such as averages and sums. The Integration Service performs aggregate calculations as it reads and stores data group and row data in an aggregate cache. The Aggregator transformation is an active transformation.
The Aggregator transformation is unlike the Expression transformation, in that you use the Aggregator transformation to perform calculations on groups. The Expression transformation permits you to perform calculations on a row-by-row basis.
When you use the transformation language to create aggregate expressions, you can use conditional clauses to filter rows, providing more flexibility than SQL language.
After you create a session that includes an Aggregator transformation, you can enable the session option, Incremental Aggregation. When the Integration Service performs incremental aggregation, it passes source data through the mapping and uses historical cache data to perform aggregation calculations incrementally.

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