Question

A retail company streams point-of-sale transactions into an Eventhouse (KQL database) in Microsoft Fabric. Analysts need a query that, for each store, continuously computes the total sales amount over the most recent 15 minutes, recalculated every 5 minutes, so overlapping periods are reported to detect sudden spikes. Which KQL windowing approach should you use to satisfy this requirement?

Accepted Answer

Use a hopping window logic where the window size is 15 minutes and the hop (advance) is 5 minutes, producing overlapping periods.

Answer

Use the summarize operator with bin() on 15-minute buckets to create fixed, non-overlapping tumbling windows.

Answer

Use the summarize operator with bin() on 5-minute buckets so every window covers exactly one interval.

Answer

Use the row_window_session() function to group transactions by inactivity gaps between purchases.