Apache Kafka - Real-time Stream Processing (Master Class) - Common Mistakes in Aggregation

The video tutorial discusses the critical considerations for computing real-time aggregates, focusing on the challenges of negative aggregation scenarios. It uses examples like customer reward points and department salary averages to illustrate these challenges. The tutorial explains the differences between K stream and K table in Kafka streams, emphasizing the importance of choosing the right abstraction for accurate aggregation. The video concludes with a summary and a preview of the next lecture.

In what scenarios should you use a K table instead of a K stream?

What is the difference between K stream and K table in Kafka streams?