Apache Kafka - Real-time Stream Processing (Master Class) - Streaming Aggregates - Core Concept

The video tutorial covers real-time aggregation in Kafka Streams, emphasizing the importance of choosing the right aggregation key and ensuring data is partitioned correctly. It explains the difference between key-preserving and key-changing APIs and how Kafka handles automatic repartitioning. The tutorial also details the aggregation functions available in Kafka Streams, such as count, reduce, and aggregate, and discusses their applications and limitations.

What are the differences between key preserving APIs and key changing APIs in Kafka Streams?

What are the three functions or methods offered by Kafka Streams to compute an aggregate?

Why is the reduce method preferred for aggregation, and what are its restrictions?