What design pattern is most suitable for building a resilient and scalable system that processes high-volume, real-time weather data streams?

For building a resilient and scalable system that processes high-volume, real-time weather data streams, the microservices architecture combined with a message queue system is the most suitable design pattern. A 'microservices architecture' is a design approach where an application is structured as a collection of small, independent services, modeled around a business domain. 'Resilience' refers to the system's ability to withstand failures and continue operating. 'Scalability' refers to the system's ability to handle increasing workloads. A 'message queue system' (e.g., Kafka, RabbitMQ) is a software component that allows different services to communicate asynchronously by sending and receiving messages. In this context, the weather data stream can be ingested by one or more microservices responsible for data acquisition. These services then publish the raw data to a message queue. Other microservices can then subscribe to the message queue and process the data independently. For example, one microservice might be responsible for cleaning and validating the data, another for storing it in a time-series database, and another for generating alerts. The microservices architecture promotes resilience because each service is independent. If one service fails, it does not necessarily affect the other services. The message queue system ensures that data is not lost if a service is temporarily unavailable. The microservices architecture also promotes scalability because each service can be scaled independently. If one service becomes overloaded, it can be scaled up by adding more instances of that service. The message queue system helps to distribute the workload across the different services. For example, if the data validation service becomes a bottleneck, more instances of that service can be added to the message queue to handle the increased load. This combination allows the system to handle a high volume of real-time weather data streams while remaining resilient and scalable.