Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Aggregate fields

Aggregate fields

Use an aggregate field to define aggregate calculations.
When you configure an Aggregator transformation, create an aggregate field for the output of each calculation that you want to use in the data flow. You can use aggregate functions in aggregate fields. You can also use conditional clauses and nonaggregate functions.
Configure aggregate fields on the
Aggregate
tab of the
Properties
panel. When you configure an aggregate field, you define the field name, data type, precision, scale, and optional description. The description can contain up to 4000 characters. You also define the calculations that you want to perform.
When you configure aggregate fields, you can use variable fields for calculations that you want to use within the transformation. You can also include macros in aggregate and variable fields.
In advanced mode, the output is NULL if the Group by field returns a single row and the aggregate expression contains the STDDEV and VARIANCE functions. This is because
Data Integration
uses Spark 3.2. To get an output value of 0, set the
spark.sql.legacy.statisticalAggregate
session property to true in the
mapping
task.

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