Apache Kafka - Real-time Stream Processing (Master Class) - Count on KTable

The video tutorial explains how to perform count, reduce, and aggregate operations on K streams and K tables. It introduces a problem statement involving counting persons by age using Kafka topics. The tutorial highlights the differences between K table and K stream aggregation, emphasizing the importance of handling updates in K tables. It demonstrates the use of group by and count methods to achieve the desired results and concludes with a preview of the next lecture on the aggregate method.

What is the primary difference between K table aggregate and K stream aggregate as discussed in the video?

• K stream aggregate can handle updates, while K table aggregate cannot.

• K stream aggregate is faster than K table aggregate.

• K table aggregate can handle updates, while K stream aggregate cannot.

• K table aggregate is used for real-time data, while K stream aggregate is not.

Why is it necessary to use K table aggregation for counting people by age?

• Because K table aggregation is faster than K stream aggregation.

• Because the age of a person can change over time.

• Because K table aggregation is easier to implement.

• Because the age of a person is constant.

What is the first step in implementing K table aggregation?

• Group the K table by name.

• Consume the Kafka topic as a K table.

• Print the K table.

• Consume the Kafka topic as a K stream.

How does the K table aggregation handle updates to data, such as a change in age?

• It ignores the updates.

• It updates the count for both the old and new ages.

• It only updates the count if the name changes.

• It updates the count for the old age but not for the new age.

What additional method is introduced for more visibility on decreasing values in K table aggregation?

• The reduce method.

• The count method.

• The filter method.

• The aggregate method.