Windowing Operations in Kafka Streams

In stream processing, data arrives continuously. To perform calculations like "total sales per hour" or "average temperature every 5 minutes," we need to group these continuous events into finite, manageable segments.

This grouping of events based on time is called windowing. It allows us to apply aggregations and transformations over specific periods.

Imagine analyzing website clicks. You might want to know:

• How many clicks happened in the last minute?
• What's the average user activity over the past 5 minutes, updated every minute?
• When did a user become inactive?

Windowing provides the framework to answer these questions by defining boundaries around your data streams.