Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator 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 Generator 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

Window functions

Window functions

In advanced mode, you can use a window function to concisely express stateful computations. A window function takes a small subset of a larger data set for processing and analysis.
Window functions operate on a group of rows and calculate a return value for every input row.
Use window functions to perform the following tasks:
  • Retrieve data from upstream or downstream rows.
  • Calculate a cumulative sum based on a group of rows.
  • Calculate a cumulative average based on a group of rows.
Before you define a window function, configure the following window properties on the
Window
tab:
Frame
Defines the rows that are included in the frame for the current input row, based on physical offsets from the position of the current input row.
You configure a frame if you use an aggregate function as a window function. The window functions LEAD and LAG reference individual rows and ignore the frame.
Partition Keys
Separates the input rows into different partitions.
If you do not define partition keys, all rows belong to a single partition.
Order Keys
Defines how rows in a partition are ordered.
The fields you choose determine the position of a row within a partition. The order key can be ascending or descending. If you do not define order keys, the rows have no particular order.
You cannot parameterize an expression that contains a window function. If the expression is parameterized, you cannot specify a window function in the mapping task.

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