In an e-commerce application, any type of transactions like purchase or payment should be modeled as KStream.

In an e-commerce application, any type of transactions like purchase or payment should be modeled as KStream.

• A. True
• B. False
• C. Only purchases
• D. Only payments

Answer: (A)

Transactions such as purchases and payments are events that occur over time, so they fit naturally into a stream of records. A KStream in Kafka Streams represents an unbounded, append-only sequence of events, which is ideal for capturing every transaction as it happens, preserving full history, and enabling real-time processing. With a KStream you can filter, transform, and enrich events, perform windowed aggregations (like total sales per hour), and join with other streams or reference data to drive downstream analytics and actions. This approach supports replaying data from the beginning and reprocessing if needed, which is essential for auditing and fault tolerance. Using a KTable instead would keep only the latest value for each key, discarding the full sequence of transactions and thus losing historical event data. While you can derive state by materializing a KTable from a KStream, the raw transactions themselves are best modeled as a stream. That’s why modeling both purchases and payments as KStreams is the correct approach.

Transactions such as purchases and payments are events that occur over time, so they fit naturally into a stream of records. A KStream in Kafka Streams represents an unbounded, append-only sequence of events, which is ideal for capturing every transaction as it happens, preserving full history, and enabling real-time processing. With a KStream you can filter, transform, and enrich events, perform windowed aggregations (like total sales per hour), and join with other streams or reference data to drive downstream analytics and actions. This approach supports replaying data from the beginning and reprocessing if needed, which is essential for auditing and fault tolerance.

Using a KTable instead would keep only the latest value for each key, discarding the full sequence of transactions and thus losing historical event data. While you can derive state by materializing a KTable from a KStream, the raw transactions themselves are best modeled as a stream. That’s why modeling both purchases and payments as KStreams is the correct approach.