Apache Kafka - Real-time Stream Processing (Master Class) - Streaming Aggregates - Core Concept

The video tutorial covers real-time aggregation in Kafka Streams, emphasizing the importance of choosing the right aggregation key and ensuring data is partitioned correctly. It explains the difference between key-preserving and key-changing APIs and how Kafka handles automatic repartitioning. The tutorial also details the aggregation functions available in Kafka Streams, such as count, reduce, and aggregate, and discusses their applications and limitations.

What is the first step in computing an aggregate in Kafka Streams?

• Repartition the data

• Change the message key

• Group data by the key

• Apply an aggregation formula

Why is it important to ensure all records for the same key are in a single partition?

• To increase data redundancy

• To ensure accurate aggregation results

• To avoid duplicate records

• To reduce processing time

What feature does Kafka Streams API provide to handle key changes during aggregation?

• Key duplication

• Automatic repartitioning

• Data compression

• Manual repartitioning

Which type of API should you use if you want to preserve the message key?

• Key-changing API

• Data-transforming API

• Key-preserving API

• Message-filtering API

What happens when you use a key-changing API followed by an aggregate or join?

• Data is compressed

• Automatic repartitioning occurs

• Data is duplicated

• Manual intervention is required

Which aggregation method is recommended for its simplicity in Kafka Streams?

• Transform

• Count

• Reduce

• Aggregate

Why can't Kafka Streams apply functions like average directly on complex data types?

• Lack of computational power

• Complex data types are not supported

• Record structure is not primitive like SQL types

• Data types are too large