Apache Kafka - Real-time Stream Processing (Master Class) - Introducing GlobalKTable

What is a key advantage of using K tables in Kafka Streams?

• They reduce network traffic by centralizing data processing.

• They enable parallel processing by working on data partitions independently.

• They allow for global data access across all stream tasks.

• They automatically synchronize data across all instances.

What is a defining characteristic of a Global K table?

• It processes data from a single partition.

• It is available to all stream tasks and can be accessed globally.

• It is local to each stream task.

• It requires no local storage on instances.

In what scenario is a Global K table most beneficial?

• When reducing storage requirements.

• When processing data in parallel across partitions.

• When performing star joins and foreign key lookups.

• When minimizing network traffic.

What is a potential drawback of using Global K tables?

• They limit the number of stream tasks.

• They cannot be used for broadcast operations.

• They require local storage on each application instance.

• They do not support parallel processing.

How does a local K table handle data processing?

• It processes data from all partitions simultaneously.

• It broadcasts data to all instances.

• It processes data from a single assigned partition.

• It requires synchronization with other stream tasks.