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?

• To manage user authentication

• To send emails

• To read and process data from Kafka topics

• To store data permanently

In a microservices architecture, why might an application prefer querying aggregated results over consuming the entire output stream?

• To save storage space

• To get specific answers like a database query

• To avoid network congestion

• To reduce data processing time

What is a suggested solution for applications needing to query specific aggregates in a microservices setup?

• Increasing server capacity

• Using a message queue

• Pushing aggregates to a database layer

• Implementing a caching mechanism

What is the benefit of adding a REST interface to the Kafka streams application?

• It increases the complexity of the system

• It allows direct querying of aggregated results

• It requires more storage

• It slows down the processing speed

What is the term used for directly querying a streams application in Kafka?

• Streamlining

• Interactive query

• Data mining

• Batch processing