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Answer: Design a distributed processing architecture that can scale out based on the incoming data volume and ensure data accuracy using a combination of real-time processing techniques and machine learning models.
To handle high data volumes and ensure data accuracy in a supply chain management system, a distributed processing architecture should be designed to scale out based on the incoming data volume. This approach ensures that the solution can handle the high throughput and low latency required for real-time tracking of goods and inventory levels. Additionally, a combination of real-time processing techniques, such as data aggregation, windowing, and stateful processing, and machine learning models can be used to improve data accuracy and provide predictive insights into inventory levels. This approach allows the system to maintain high performance and accuracy, even under high data volumes.
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You are developing a stream processing solution for a supply chain management system that tracks the movement of goods and monitors inventory levels in real-time. The system needs to handle high data volumes and ensure data accuracy. How would you design the solution to meet these requirements?
A
Use a single centralized processing engine to handle all incoming data, without considering the scalability or fault tolerance.
B
Design a distributed processing architecture that can scale out based on the incoming data volume and ensure data accuracy using simple threshold-based rules.
C
Implement a machine learning model to predict inventory levels, but do not consider the real-time requirements or data accuracy.
D
Design a distributed processing architecture that can scale out based on the incoming data volume and ensure data accuracy using a combination of real-time processing techniques and machine learning models.
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