Table of Contents

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

Transformations

Transformations

Example: Run an aggregate function on a window

Example: Run an aggregate function on a window

You want to compare the salary of each employee with the average salary for the corresponding department.
The following table lists the department names, the employee identification number, and the employee salary:
Department
Employee
Salary
Development
11
5200
Development
7
4200
Development
9
4500
Development
8
6000
Development
10
5200
Personnel
5
3500
Personnel
2
3900
Sales
3
4800
Sales
1
5000
Sales
4
4800
You set an unbounded frame to include all employees in the calculation, and you define an aggregate function to calculate the difference between the salary of each employee and the average salary in the department.

Window Properties

You define the following window properties on the
Window
tab:
Property
Value
Description
Start offset
All Preceding Rows
Describes the number of rows that appear before the current input row.
End offset
All Following Rows
Describes the number of rows that appear after the current input row.
Order key
Salary Ascending
Arranges the data by increasing salary.
Partition key
Department
Groups the rows according to department.
When you select All Preceding Rows and All Following Rows, the function includes all partition rows. For example, suppose the current row is the third row. The third row is in the "Development" partition, so the frame includes the third row in addition to all rows before and after the third row in the "Development" partition.

Window Function

Because you configure window properties in the Expression transformation, you can use an aggregate function as a window function.
You define the following aggregate function to calculate the difference between the salary of each employee and the average salary in the corresponding department:
Salary - AVG ( Salary ) = Salary_Diff

Output

The transformation produces the following salary differences:
Department
Employee
Salary
Salary_Diff
Development
11
5200
-820
Development
7
4200
-520
Development
9
4500
180
Development
8
6000
180
Development
10
5200
980
Personnel
5
3500
200
Personnel
2
3900
200
Sales
3
4800
-66
Sales
1
5000
-66
Sales
4
4800
134
You can identify which employees are making less or more than the average salary within the same department. Based on this information, you can add other transformations to learn more about the data. For example, you might add a Rank transformation to produce a numerical rank for each employee within the same department.