Windowing and Watermarks

Concepts: paLateData

What They Want to Hear 'I choose the window type based on the use case. Tumbling windows are fixed, non-overlapping intervals: count clicks per hour. Sliding windows overlap: count clicks in the last hour, updated every 5 minutes. Session windows are activity-based: group events that are close together in time, with a gap timeout. I set the watermark based on observed lateness: if 99th percentile lateness is 5 minutes, I set the watermark to 10 minutes to catch stragglers with margin.' This is the answer that shows you can match window type to use case and set watermarks from data.