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?

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

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What are the three arguments required by the aggregate method?

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How does the aggregator function in the aggregate method?

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