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

The video tutorial explains the use of K tables for aggregation in Kafka streams, focusing on handling update streams. It covers the transition from K stream to K table aggregation, emphasizing the need for updates when employee data changes. The tutorial details the grouping and aggregation process in K tables, highlighting the differences from K streams. It introduces the adder and subtractor aggregators, explaining their roles in managing updates. The video concludes with executing the solution and observing the results, suggesting the use of a state store query server for better insights.

Describe the process of how the group by operation works on a K table.

What is the main difference between a K table and a K stream in terms of data aggregation?

How does the initializer function in a K table aggregate work?

Explain the role of the adder and subtractor in the aggregation process.

What happens when an employee's department is updated in the K table?

How does the aggregation mechanism adjust for new and old records in the group?

What is the significance of the state store in Kafka streams?