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Scaling to the Cloud: Docker to AWS Migration Playbook

Gerald M
12 min read
2025-02-05

Docker to AWS: A Migration Playbook

Large-scale infrastructure migrations are risky. One misconfiguration can cascade into downtime affecting millions of users. Between 2022-2023, I led the migration of our Docker-based platform to AWS across multiple data centers globally. Here's what we learned.

The Starting Point

Our platform ran on:

  • On-premise Docker Swarm clusters
  • Multiple regional data centers lacking elasticity
  • Limited auto-scaling capabilities
  • High operational overhead

We needed:

  • Cloud-native infrastructure
  • Regional redundancy
  • Cost optimization
  • Faster deployment cycles

Migration Strategy: "Big Bang" vs. Gradual

We chose gradual migration with parallel runs:

  • Keep existing infrastructure running during transition
  • Migrate workloads progressively to AWS
  • Validate each service before decommissioning legacy infrastructure
  • Built confidence through staged rollout

The Three Phases

Phase 1: Foundation (Months 1-3)

  • Set up AWS infrastructure (VPC, RDS, ALB)
  • Containerize remaining monolithic services
  • Build CI/CD pipelines with GitHub Actions
  • Implement monitoring (CloudWatch + custom dashboards)

Phase 2: Batch Migration (Months 4-7)

  • Migrated stateless services first (API gateways, workers)
  • Databases: Replicated data to RDS, validated consistency
  • Load balancing: Blue-green deployments for zero-downtime switches
  • Rollback plans for every service

Phase 3: Data Layer Migration (Months 8-10)

  • PostgreSQL migration with minimal downtime
  • Redis cluster failover
  • Elasticsearch cluster reconstruction
  • Archived legacy infrastructure

Key Decisions

Cost Optimization:

  • Reserved Instances for baseline + Spot for variable workloads
  • Auto-scaling groups for traffic spikes
  • Transitioned to serverless for specific microservices

Reliability:

  • Multi-AZ deployments for high availability
  • Automated backups with cross-region replication
  • Health checks every 30 seconds with automatic failover

Governance:

  • Infrastructure as Code (Terraform) for reproducibility
  • Separate dev/staging/prod environments
  • Role-based access control with IAM policies

Results

  • 80% of workloads migrated to AWS within 10 months
  • 60% cost reduction despite increased capacity (via optimization)
  • 99.95% uptime during migration (0 production incidents)
  • 40% faster deployments with automated CI/CD

Lessons Learned

  1. Change management matters - Your infrastructure team needs clear communication and runbooks
  2. Test extensively - We ran parallel systems for 3 months to catch edge cases
  3. Automation first - Manual verification doesn't scale; invest in automated testing
  4. Monitor everything - Logs, metrics, and traces are your safety net
  5. Plan for rollback - Every migration step needs a clear rollback procedure

The Final Outcome

The migration transformed our operational capability:

  • Developers can now spin up environments in minutes
  • Scaling from 100 to 10,000 requests/sec is automatic
  • Regional deployment is now a single command
  • Operational burden reduced from 3 FTE to 1 FTE

The infrastructure is now elastic, cost-effective, and maintainable—enabling engineers to focus on product development rather than infrastructure management.