Case Study – MyBank

The video tutorial discusses implementing real-time banking alerts using Kafka. It covers the architecture needed to process transaction data from an SQL database using Kafka Connect and CDC connectors like dbisam. The tutorial explains setting user-defined thresholds for alerts and how Kafka streams can match transactions against these thresholds to generate alerts. It emphasizes the importance of using CDC connectors for efficient data exposure and highlights best practices for event-driven architecture.

What is the primary challenge the digital bank is trying to address?

• Improving user interface design

• Enhancing customer service

• Detecting fraudulent transactions in real-time

• Reducing transaction fees

Which tool is suggested for capturing changes from the SQL database to Kafka?

• Kafka Streams

• Kafka Producer

• Debezium CDC Connector

• Kafka Consumer

What is the role of the user settings topic in the proposed architecture?

• To store transaction data

• To log system errors

• To hold user-defined alert thresholds

• To manage user authentication

Why is it recommended to send events rather than states to Kafka topics?

• States require more storage space

• Events are easier to process

• States are more complex to manage

• Events provide more historical context

What is a key advantage of using CDC connectors in Kafka?

• They enable real-time data capture

• They enhance data security

• They simplify data transformation

• They reduce network latency