May 2019
Intermediate to advanced
442 pages
11h 36m
English
Cross-sectional aggregations are the easiest to understand. As we can see in the following data representation, we take a column of data and apply an aggregation function to it:
| Location/Time | t=0 | t=1 | t=2 | t=3 | t=4 | t=5 | t=6 |
| Factory | 1,045 | 1 | 2 | 3 | 4 | 5 | 6 |
| Warehouse | 223 | 223 | 223 | 223 | 224 | 224 | 224 |
| Headquarters | 40160 | 40,162 | 40,164 | 40,166 | 40,168 | 40,170 | 40,172 |
If we apply the max() aggregation, we can find out which location reported more coffees were dispensed—in this case, the result would be 40,172. Applying count() would give us the number of offices reporting data for the dimensions that were selected ({company=ACME, beverage=coffee}): 3.
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