Windowing in Apache Kafka Streams is a technique used to process data over time. It allows developers to define specific time-based windows within which data can be aggregated or processed. There are four types of windowing: hopping, tumbling, session, and sliding. Hopping windows advance at regular intervals, while tumbling windows do not overlap with each other. Session windows are triggered by events and have an inactivity gap, whereas sliding windows require user events to happen within a defined time frame. These techniques enable developers to analyze data over specific time periods, handle late-arriving events, and introduce grace periods for skew analysis. Understanding windowing is essential for processing and analyzing data in real-time applications using Apache Kafka Streams.