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Windowing in stream processing allows for real-time responses to events. For instance, immediate responses are critical for scenarios such as fraud detection and monitoring manufacturing processes. Some event responses are tied to several events over a timeframe rather than an individual event. The concept of windowing in event streams is observing these events in a fixed period. Windowing is useful in yielding insights on specific activities over time rather than an aggregation of all events. The average value in particular intervals is more informative than an overall average. Kafka Streams and Flink SQL, the two dominant stream processing technologies known for their windowing operations, will be observed in a blog series. The series will discuss various aspects of windowing, such as types, semantics, use cases, time semantics, result interpretation, and testing. The post uses aggregation examples in Kafka Streams and Flink SQL to illustrate the windowing process. Note: This is the first in a series about windowing in Kafka Streams and Flink SQL, you are expected to have basic knowledge of these technologies. The subsequent posts will dive more into windowing but with the assumption that you have an understanding how to assemble the program from this initial post. Resources provided for more information include official documentation for Kafka and Flink, as well as a suggested book - "Kafka Streams in Action 2nd Edition".

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