You're tasked with migrating a monolithic e-commerce backend to a microservices architecture. Describe your strategy for decomposing the monolith, identifying service boundaries, managing data consistency across services, and ensuring a smooth, zero-downtime transition for users.

MECE Framework: 1. Decompose: Identify bounded contexts (domain-driven design) for core business capabilities (e.g., Catalog, Order, User, Payment). Prioritize high-change, high-scale modules. 2. Boundaries: Define clear API contracts (REST/gRPC) for inter-service communication. Use Conway's Law to align teams. 3. Data Consistency: Implement eventual consistency patterns (Saga, CDC, Outbox) for distributed transactions. Utilize a shared message bus (Kafka) for event-driven updates. 4. Transition: Employ Strangler Fig Pattern for incremental migration. Use feature toggles and A/B testing. Implement robust monitoring, canary releases, and automated rollbacks for zero-downtime deployment.

My strategy for decomposing an e-commerce monolith leverages the MECE framework for a comprehensive, structured approach. First, I'd apply Domain-Driven Design to identify natural bounded contexts, such as 'Catalog,' 'Order Management,' 'User Profile,' and 'Payment Gateway.' Prioritization would focus on modules exhibiting high change frequency, scalability bottlenecks, or independent team ownership. Service boundaries would be rigorously defined with clear API contracts (e.g., OpenAPI for REST, Protobuf for gRPC), ensuring loose coupling and high cohesion. For data consistency across these distributed services, I'd implement eventual consistency patterns like the Saga pattern for complex workflows or Change Data Capture (CDC) with an Outbox pattern for transactional integrity, leveraging a robust message broker like Kafka. The transition would employ the Strangler Fig Pattern, incrementally extracting services behind an API Gateway. This allows for parallel operation of old and new systems. Zero-downtime is ensured through canary deployments, feature toggles, comprehensive observability (metrics, logs, traces), and automated rollback mechanisms, minimizing user impact.