Apache Kafka - Real-time Stream Processing (Master Class) - Introducing Micro-services Requirement

The video tutorial explains how Kafka streams applications read data from Kafka topics and process it to compute aggregates in real-time. These aggregates are stored in local state stores and can be materialized back to Kafka topics for other applications to consume. In microservices architecture, applications may need to query specific aggregated results without consuming the entire stream. This can be achieved by pushing aggregates to a database or by using Kafka streams' interactive query feature, which allows direct querying of the application state store. This approach simplifies the architecture, reduces IO, and improves performance.

What is the primary function of a Kafka streams application?

How are aggregated results materialized in a Kafka streams application?

What challenges arise when applications want to query aggregated results?

Describe the benefits of adding a request-response capability to a Kafka streams application.

What is the interactive query feature of Kafka streams, and why is it significant?