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 method is introduced in this lecture for aggregation in Kafka Streams?

• Count method

• Reduce method

• Filter method

• Map method

What is the main goal of rewriting the rewards computation example?

• To use a custom state store

• To eliminate the need for a manual state store

• To introduce a new Kafka topic

• To change the notification format

What is the first step in transforming filtered invoices into notifications?

• Apply a map method

• Use the reduce method

• Change the key to store ID

• Group by customer ID

Why is the key changed to customer ID during the transformation process?

• To simplify the notification format

• To compute an aggregate on customer ID

• To use a default service

• To ensure data is partitioned by store ID

What does the reduce method do in the context of this example?

• Filters the stream

• Calculates the total rewards for the customer

• Maps values to a new format

• Changes the key to store ID

How can you verify that the reward is calculated correctly?

• By using a console consumer

• By changing the key to store ID

• By using a default service

• By applying a filter method

What is suggested for testing stream processing applications?

• Changing the key to store ID

• Using a default service

• Automating the testing

• Using a manual method