Imagine you're designing a new broadcast control room for a major sports network. How would you approach the system architecture to ensure high availability, fault tolerance, and scalability for simultaneous live events, considering both on-premise and cloud-based solutions?

MECE Framework: I'd segment the architecture into four pillars: Ingest & Processing, Storage & Archiving, Distribution & Delivery, and Monitoring & Control. For Ingest, prioritize redundant encoders (on-prem) with cloud-based transcoding for diverse formats. Storage would be hybrid: high-speed SAN/NAS (on-prem) for active production, tiered cloud object storage for archiving and disaster recovery. Distribution leverages CDN integration (cloud) for global reach, with on-premise playout servers for primary feeds. Monitoring integrates AI/ML for anomaly detection across both environments, ensuring proactive fault tolerance. Scalability is achieved via microservices architecture, containerization (Kubernetes), and auto-scaling groups in the cloud, complemented by modular on-prem hardware for rapid expansion.

I'd apply the MECE framework to design a robust, scalable architecture. For Ingest and Processing, I'd implement a hybrid model: redundant on-premise encoders (e.g., Evertz, Imagine Communications) for low-latency acquisition, paired with cloud-based transcoding services (e.g., AWS Elemental MediaLive, Google Cloud Media CDN) for format diversity and burst capacity. Storage would be tiered: high-performance on-premise SAN/NAS for active production assets, seamlessly integrated with cloud object storage (e.g., S3, Azure Blob) for long-term archiving and disaster recovery. Distribution and Delivery would leverage a global CDN (e.g., Akamai, Cloudflare) for audience reach, with on-premise playout servers handling primary linear feeds and cloud-based origin servers for OTT. Fault tolerance is built-in via active-active redundancy for all critical path components, automated failover mechanisms, and geo-redundant cloud deployments. Scalability is achieved through a microservices-based architecture, containerization (Kubernetes), and auto-scaling groups in the cloud, allowing resources to dynamically adjust to demand. Comprehensive monitoring and alerting (e.g., Datadog, Prometheus) across both environments would ensure proactive issue detection and resolution.