Migrating a legacy system to the cloud requires a phased approach to ensure scalability. First, assess the system’s architecture to identify monolith components that can be refactored into microservices. Prioritize containerization (Docker, Kubernetes) and auto-scaling services (AWS ECS, Lambda). For databases, consider managed options like Amazon RDS or DynamoDB with read replicas. A real-world example: I once supported a monolith-to-microservices transition where we used an API Gateway and event-driven architecture to handle spikes seamlessly. Scaling isn’t just about infra—it’s about designing for elasticity from day one.

1. Assess & Plan Conduct a thorough workload assessment to identify dependencies, risks, and migration priorities. Choose the right migration strategy: Rehost (lift-and-shift), Refactor, Rebuild, or Replace. 2. Optimize Architecture for Scalability Use auto-scaling and load balancing to handle fluctuating workloads. Opt for containerization (Docker, Kubernetes) to improve portability and scalability. 3. Implement Cloud-Native Solutions Use serverless computing (AWS Lambda, Azure Functions) for dynamic scaling. Leverage managed databases like AWS RDS or Azure SQL for performance optimization.

Migrating a legacy system to the cloud requires careful planning. First, I’d assess the system to identify key components and bottlenecks. Choosing a cloud provider that aligns with the system’s needs—considering scalability, security, and cost—is crucial. I’d break the migration into smaller, manageable phases to minimize risk and enable continuous testing. Automation tools and CI/CD pipelines would streamline the process. Post-migration, constant monitoring ensures the system remains robust, addressing issues proactively and scaling seamlessly as business needs evolve. This approach helps maintain efficiency while minimizing disruptions.