Consider a scenario where an existing operational system is experiencing frequent outages due to unexpected data spikes. How would you approach redesigning the system architecture to handle these unpredictable loads, ensuring both data integrity and continuous service availability?

Employ a MECE (Mutually Exclusive, Collectively Exhaustive) framework. First, analyze current architecture: identify bottlenecks, data flow, and spike characteristics. Second, design a scalable solution: implement auto-scaling compute resources (e.g., Kubernetes, serverless functions), distributed databases (e.g., Cassandra, MongoDB) for horizontal scaling, and message queues (e.g., Kafka, RabbitMQ) for asynchronous processing and load leveling. Third, ensure data integrity: implement robust validation, idempotent operations, and transactional consistency mechanisms. Fourth, guarantee continuous availability: deploy redundant components, failover mechanisms, and comprehensive monitoring with alerting. Fifth, test rigorously: conduct load, stress, and chaos engineering tests. Finally, implement phased rollout and continuous optimization.

I would approach this using a phased strategy, prioritizing resilience and scalability. Initially, I'd conduct a thorough system audit to pinpoint the exact sources and characteristics of data spikes, leveraging monitoring tools and log analysis. This involves mapping data flows, identifying choke points, and understanding dependencies. Next, I'd propose an architectural redesign focusing on horizontal scalability and asynchronous processing. This would include implementing auto-scaling compute resources (e.g., container orchestration like Kubernetes or serverless functions), introducing message queues (e.g., Kafka) to decouple services and buffer incoming data, and migrating critical data stores to distributed, highly available databases capable of handling high write/read throughput. For data integrity, I'd implement robust validation at ingestion points, ensure idempotent operations, and utilize transactional consistency mechanisms where necessary. Continuous service availability would be addressed through redundancy, failover strategies, and comprehensive real-time monitoring with automated alerts. Finally, I'd advocate for rigorous load and stress testing before a phased rollout, followed by continuous performance tuning and optimization.