Apache Kafka - Real-time Stream Processing (Master Class) - Supressing Intermediate Results

The lecture discusses suppressing intermediate results in stream processing, focusing on a use case involving application monitoring with Kafka streams. It explains how to count heartbeats, send alerts, and handle continuous updates. The lecture highlights the importance of suppressing intermediate results to avoid false alerts and ensure accurate monitoring. A practical example demonstrates the implementation, emphasizing the need for a grace period and suppression to manage intermediate results effectively.

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