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1. Preface
2. Introduction to Informatica Big Data Management
3. Mappings
4. Sources
5. Targets
6. Transformations
7. Data Preview
8. Cluster Workflows
9. Profiles
10. Monitoring
11. Hierarchical Data Processing
12. Hierarchical Data Processing Configuration
13. Hierarchical Data Processing with Schema Changes
14. Intelligent Structure Models
15. Stateful Computing
16. Connections
17. Data Type Reference
18. Function Reference

# Aggregate Functions as Window Functions

In addition to LEAD and LAG, you can also use aggregate functions as window functions. When you use aggregate functions like SUM and AVG as window functions, you can perform running calculations that are similar to the stateful functions MOVINGSUM, MOVINGAVG, and CUME. Window functions are more flexible than stateful functions because you can set a specific end offset.
To use an aggregate function as a window function, you must define a frame in the windowing properties. You define a frame to limit the scope of the calculation. The aggregate function performs a calculation across the frame and produces a single value for each row.

## Example

You are a lumber salesperson who sold different quantities of wood over the past two years. You want to calculate a running total of sales quantities.
The following table lists each sale ID, the date, and the quantity sold:
`Sale_ID`
`Date`
`Quantity`
`30001`
`2016-08-02`
`10`
`10001`
`2016-12-24`
`10`
`10005`
`2016-12-24`
`30`
`40001`
`2017-01-09`
`40`
`10006`
`2017-01-18`
`10`
`20001`
`2017-02-12`
`20`
A SUM function adds all the values and returns one output value. To get a running total for each row, you can define a frame for the function boundaries.
You configure the following windowing properties:
• Start offset: All Rows Preceding
• End offset: 0
• Order Key: Date Ascending
• Partition Key: Not specified
You define the following aggregate function:
`SUM (Quantity)`
SUM adds the quantity in the current row to the quantities in all the rows preceding the current row. The function returns a running total for each row.
The following table lists a running sum for each date:
`Sale_ID`
`Date`
`Quantity`
`Total`
`30001`
`2016-08-02`
`10`
`10`
`10001`
`2016-12-24`
`10`
`20`
`10005`
`2016-12-24`
`30`
`50`
`40001`
`2017-01-09`
`40`
`90`
`10006`
`2017-01-18`
`10`
`100`
`20001`
`2017-02-12`
`20`
`120`
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