After cleaning and structuring data, you often need to summarize it. Instead of looking at every single transaction or event, you might want a higher-level view, like total sales per day or the number of users per city. This process of summarizing data is called aggregation.
Aggregation involves grouping rows that share common attribute values and then applying a calculation across those groups to produce a single summary value for each group. Think of it as condensing large amounts of detailed information into more digestible summaries.
Aggregation is a fundamental transformation step for several important reasons:
While many calculations can be used, some aggregation functions are particularly common in ETL processes:
Imagine you have extracted raw sales transaction data that looks something like this:
TransactionID | Product | Region | SalesAmount |
---|---|---|---|
1 | Widget A | North | 100 |
2 | Widget B | South | 150 |
3 | Widget A | North | 120 |
4 | Widget C | East | 80 |
5 | Widget B | North | 90 |
6 | Widget A | South | 110 |
This detailed data is useful, but perhaps the target analytical system only needs to know the total sales for each region. You can achieve this through aggregation.
You would group the data by the Region
column and apply the SUM()
function to the SalesAmount
column for each group. The transformation process would produce the following summarized table:
Region | TotalSales |
---|---|
North | 310 |
South | 260 |
East | 80 |
This aggregated table is much smaller and directly answers the question, "What are the total sales per region?"
Total sales amount aggregated for each region.
Data aggregation is a powerful transformation technique used to distill large datasets into meaningful summaries, preparing them for effective analysis and reporting during the ETL process. It's a standard operation performed after initial cleaning and structuring to shape data for its final destination.
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