Which type of search is primarily used for aggregating data in Splunk?

The type of search primarily used for aggregating data in Splunk is transforming search. Transforming searches are specifically designed to process and summarize large datasets, allowing users to perform operations such as count, stats, and other forms of data calculations. These types of searches typically return a different set of results than what the original data contained, as they manipulate the data by applying aggregations and calculations.

Transforming searches handle commands that allow you to summarize, group, and extract insights from the data, making them highly effective for analytical purposes. Aggregate functions like stats, timechart, and chart fall into this category, as they allow for complex calculations that summarize the data into meaningful formats.

In contrast, streaming searches operate on events as they are being processed and return results in real-time, which is useful for searching large datasets but less focused on aggregation. Non-streaming searches retrieve all events and then process them after fetching, making them less optimal for instance when working with large amounts of data compared to transforming searches. Centralized is not a recognized category of search in Splunk, thus not applicable in this context.