Use a single centralized processing engine to handle all incoming data, without considering the scalability or fault tolerance.

Design a distributed processing architecture that can scale out based on the incoming data volume and ensure data accuracy using simple threshold-based rules.

Implement a machine learning model to predict inventory levels, but do not consider the real-time requirements or data accuracy.

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.