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.

Why is a K table preferred over a K stream for update streams?

• K streams do not support aggregation.

• K tables can handle updates by maintaining state.

• K tables are easier to implement.

• K tables are faster than K streams.

What is a key difference between grouping by in K tables and K streams?

• K tables group by is slower.

• K streams group by requires a separate value.

• K tables group by returns a key-value pair.

• K streams group by does not support keys.

Which of the following is NOT an argument for K table aggregation?

• Multiplier

• Adder

• Subtractor

• Initializer

How does the subtractor function in K table aggregation?

• It divides the total salary by the count.

• It increases the count and adds to the total salary.

• It decreases the count and subtracts from the total salary.

• It multiplies the count and salary.

What happens when a record is updated in a K table?

• The record is duplicated.

• The record is ignored.

• The old record is deleted and a new one is inserted.

• The old record is modified directly.

What is the purpose of the state store query server in the application?

• To add more records.

• To delete old records.

• To directly view the state store.

• To execute the application faster.

What is the final step after sending the events in the application?

• Check the average for all departments.

• Delete all records.

• Restart the Kafka services.

• Add more departments.