Azure to AWS Migration: A Complete Enterprise Guide
Step-by-step methodology for migrating enterprise workloads from Azure to AWS using the Migration Acceleration Program.
Migrating from Azure to AWS is a complex process that requires careful planning and execution. This guide walks you through the proven methodology we use at BPS Dynamic to help enterprises make the transition smoothly.
Assessment & Planning
The first phase involves a comprehensive assessment of your current Azure infrastructure. We catalog every workload, dependency, and data flow to create a complete migration map. This assessment typically takes 2-4 weeks depending on the complexity of your environment. Key deliverables include a workload inventory, dependency mapping, and a preliminary cost comparison between your current Azure spend and projected AWS costs.
Migration Strategy Selection
Not every workload should be migrated the same way. We use the 6 Rs framework to determine the optimal strategy for each workload. For example, a simple VM running a stateless web application can be rehosted quickly, while a complex database cluster might benefit from replatforming to Aurora.
Execution & Validation
With the plan in place, we execute the migration in waves, starting with low-risk workloads to build confidence and refine our processes. Each wave follows a rigorous cutover process: pre-migration testing, data synchronization, DNS switchover, and post-migration validation. We maintain rollback capabilities throughout to ensure business continuity.
Security & Compliance Considerations
Security must be addressed at every stage of the migration. We implement AWS Identity and Access Management (IAM) policies that mirror your existing Azure RBAC configurations. Data encryption is maintained both in transit and at rest using AWS KMS. For regulated industries, we ensure compliance with frameworks like SOC 2, HIPAA, and PCI-DSS throughout the migration process.
Post-Migration Optimization
The migration does not end at cutover. We conduct a 30-day post-migration review to identify optimization opportunities. This includes right-sizing instances based on actual utilization data, implementing Reserved Instances for stable workloads, and configuring auto-scaling policies. Most clients see an additional 15-20% cost reduction during this optimization phase.