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 detect anomalies using simple threshold-based rules.

Implement a machine learning model to detect anomalies in energy usage patterns, but do not consider real-time processing requirements.

Design a distributed processing architecture that can scale out based on the incoming data volume and implement a machine learning model to detect anomalies in real-time.