Imagine you're tasked with designing a highly available, fault-tolerant, and globally distributed system for a new product with stringent uptime requirements (e.g., 99.999%). How would you approach the architectural design, considering aspects like data consistency (e.g., CAP theorem implications), disaster recovery strategies, and latency optimization for users across different geographies? Detail your thought process using a structured approach.

MECE Framework: 1. Requirements Analysis (99.999% uptime, global distribution, data consistency, low latency). 2. Architectural Pillars (Scalability, Reliability, Maintainability, Security, Performance). 3. Technology Selection (Cloud-native, microservices, polyglot persistence, CDN). 4. Data Strategy (CAP Theorem: prioritize Availability/Partition Tolerance, eventual consistency for global reads, strong consistency for critical writes via Paxos/Raft). 5. Disaster Recovery (Active-Active multi-region deployment, automated failover, RTO/RPO objectives). 6. Latency Optimization (Edge computing, global load balancing, data locality). 7. Observability (Monitoring, logging, tracing). 8. Iterative Refinement (A/B testing, chaos engineering).

My approach leverages the MECE framework, starting with a deep dive into requirements: 99.999% uptime, global distribution, strong consistency for critical operations, and low latency. Architecturally, I'd propose a cloud-native, microservices-based system deployed in an active-active multi-region configuration across major cloud providers (e.g., AWS, Azure) for true disaster recovery and vendor diversity. For data consistency, I'd apply the CAP theorem judiciously: prioritizing availability and partition tolerance with eventual consistency for read-heavy, globally distributed data (e.g., using DynamoDB Global Tables or Cassandra), while employing strong consistency (e.g., Paxos/Raft-based consensus) for critical transactional data within a regional boundary. Disaster recovery involves automated cross-region failover, regular DR drills, and defined RTO/RPO objectives. Latency optimization would utilize global load balancing (e.g., Anycast DNS), Content Delivery Networks (CDNs), edge computing for static assets and API gateways, and data locality strategies to serve users from the nearest region. Observability (monitoring, logging, tracing) is paramount for proactive issue detection and rapid resolution, ensuring the stringent uptime SLA.