Case Study – MySocialMedia

The video introduces CQRS (Command Query Responsibility Segregation) and its application in a social media context, focusing on handling high data volumes with Kafka. It explains the architecture involving separate topics for posts, likes, and comments, and the use of Kafka Streams for data aggregation and trending post identification. The video also covers the decoupling of commands and queries in CQRS for scalability and discusses data management in Kafka, including event formatting and partitioning strategies.

What does CQRS stand for in the context of system architecture?

• Command Queue Response System

• Centralized Query Routing System

• Central Query Response System

• Command Query Responsibility Segregation

In the proposed Kafka architecture, why are separate topics created for posts, likes, and comments?

• To reduce the number of producers

• To simplify the data model

• To increase the number of partitions

• To handle different data types and volumes efficiently

What is the primary role of Kafka Streams in the architecture?

• To delete outdated data

• To aggregate data from multiple topics

• To store data permanently

• To produce data to Kafka topics

How does the architecture determine trending posts?

• By analyzing the number of likes and comments

• By tracking the number of views

• By monitoring the number of reposts

• By counting the number of shares

What is the significance of decoupling commands and queries in CQRS?

• It reduces the number of database queries

• It increases data redundancy

• It enhances system scalability

• It simplifies the user interface

Why is it important to choose the right key for Kafka topics?

• To ensure data is evenly distributed across partitions

• To increase the number of producers

• To reduce the number of consumers

• To simplify the data model

How should data be formatted when produced to Kafka?

• As JSON objects

• As plain text

• As events

• As binary data