In Part-6 of this series, we will re-explore the different Windowing techniques of Kafka Streams for aggregation, but with two changes – first is to extract the Kafka message timestamp and second is to use an in-memory state store. We will implement and demonstrate the concepts using a simple application in Java. Here is the link to the article:
Exploring Kafka Streams :: Part 6
Enjoy 🙂 !!!