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Answer: Design a distributed processing architecture that can scale out by adding more processing nodes based on the incoming data volume.
To process data in real-time and scale resources based on the incoming data volume, a distributed processing architecture should be designed. This architecture can scale out by adding more processing nodes as the data volume increases. This approach ensures that the solution can handle high throughput and low latency, even during peak traffic periods. Additionally, the distributed architecture provides fault tolerance and improves the overall reliability of the system.
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You are tasked with developing a stream processing solution for a smart city project that monitors and analyzes traffic data from various sources, such as cameras, sensors, and mobile devices. The solution needs to process data in real-time and scale resources based on the incoming data volume. How would you design the solution to achieve this?
A
Use a single centralized processing engine to handle all incoming data, regardless of the volume.
B
Design a distributed processing architecture that can scale out by adding more processing nodes based on the incoming data volume.
C
Process the data in real-time using a single-threaded approach, without considering scalability.
D
Use a batch processing approach to process the data in large batches, ignoring the real-time requirements.