Apache Kafka - Real-time Stream Processing (Master Class) - KStream Aggregation using Reduce()

This video tutorial introduces aggregation in Kafka streams, focusing on using the reduce method for aggregation. It revisits a rewards computation example, demonstrating how to eliminate manual state stores by using K stream. The tutorial provides a starter project setup and guides through transforming invoices into notifications, grouping by customer ID, and reducing to compute total rewards. It concludes with testing methods and encourages further learning.

What is the purpose of using the reduce method in Kafka Streams?

What are the advantages of using a starter project for practice in Kafka Streams?

What steps are involved in transforming filtered invoices into notifications?

How does changing the key from store ID to customer ID affect the stream processing?

Explain the significance of grouping by customer ID in the aggregation process.

What is the role of the state store in the reduce method?

Describe how the notification is computed and sent to a new Kafka topic.