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

The lecture explains the mechanics of the aggregate method in stream processing, highlighting its ability to change data types, unlike the reduce method. A problem is defined to compute average salaries by department using Kafka streams, and a solution is demonstrated with a focus on grouping and aggregation. The lecture concludes with a detailed code walkthrough of the KStream aggregate demo, emphasizing the importance of initializers, aggregators, and serializers.

What is the expected output when computing the average salary for each department?

What is the significance of maintaining total salary and count in the aggregate method?

What are the three arguments required by the aggregate method?

How does the aggregator function in the aggregate method?