Apache Kafka - Real-time Stream Processing (Master Class) - Mixing Joins with Aggregates - Computing Top 3

The video tutorial explains how to implement joins and aggregation in Kafka streams, focusing on a custom solution to sort KTables by click counts to find the top three news types. It covers the problem definition, the creation of a custom data structure, and the coding of a class to manage sorted news types. The tutorial concludes with the execution of the solution, demonstrating the real-time sorting and updating of news types based on click counts.

What are the two fundamental operations discussed in the context of Kafka streams applications?

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Explain the challenge of sorting a K table by the number of clicks.

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Discuss the reasons why Kafka does not provide an API for sorting K tables.

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What steps are involved in implementing the group by operation on a K table?

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What is the purpose of creating a custom data structure for sorted news types?

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Describe the process of maintaining only the top three records in the sorted news types.

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How can the top three sorted news types class handle updates to existing records?

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What is the significance of making data structures serializable in Kafka?

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What are the three mandatory arguments required by the aggregate method in Kafka streams?

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How does the adder method function in the context of the aggregate method?

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