Platform Migration
Overview
This skill provides comprehensive guidance for migrating infrastructure platforms, cloud providers, container orchestration systems, CI/CD pipelines, and observability stacks. Focus is on infrastructure-level migrations that complement application modernization efforts.
Platform migrations are infrastructure-focused and should be paired with application migration strategies. Use cloud-neutral design patterns to reduce future migration effort, though multi-cloud strategies increase complexity while reducing vendor lock-in.
Core Migration Principles
Key Practices:
- Test everything in non-production first
- Automate using Infrastructure as Code (IaC)
- Migrate incrementally rather than "big bang"
- Maintain both platforms during stabilization
- Budget 20-30% more time than estimated
- Document architecture decisions and rationale
Avoid These Pitfalls:
- Migrating everything at once without phased approach
- Skipping non-production validation
- Ignoring cost estimation for target platform
- Failing to optimize during migration (pure lift-and-shift)
- Inadequate monitoring during transition
- Leaving resources running on both platforms post-migration
Migration Workflow
Step 1: Assessment and Planning
Identify Migration Type:
- Cloud-to-cloud (AWS↔GCP↔Azure)
- Kubernetes cluster migration (EKS→GKE→AKS)
- CI/CD platform change (Jenkins→GitLab→GitHub Actions)
- Monitoring stack migration (Prometheus→Datadog→New Relic)
- Container registry migration (Docker Hub→ECR→GCR→ACR)
- Network infrastructure transformation
- Infrastructure as Code migration (Terraform→CloudFormation→Bicep)
For detailed migration type workflows: See migration-types.md
Step 2: Inventory and Dependencies
- Document all resources in source platform
- Map dependencies between components
- Identify proprietary services without direct equivalents
- Estimate costs in target platform
- Assess technical constraints and requirements
Step 3: Design Target Architecture
- Map source services to target equivalents
- Design network topology and connectivity
- Plan IAM and security model
- Define migration approach (phased, parallel, cutover)
- Create rollback procedures
For IaC-specific migrations: See infrastructure-as-code-migration.md
Step 4: Risk Assessment and Mitigation
- Identify critical dependencies and single points of failure
- Plan for downtime windows
- Establish success criteria and validation tests
- Define rollback triggers and procedures
For comprehensive risk planning: See risk-management.md
Step 5: Pilot Migration
- Select non-critical workload for pilot
- Execute migration following documented procedure
- Validate functionality and performance
- Measure costs and resource utilization
- Document lessons learned and adjust plan
Step 6: Production Migration
- Migrate in planned waves/phases
- Implement data replication and synchronization
- Execute DNS cutover per service
- Monitor performance and errors continuously
- Validate each wave before proceeding
For validation procedures: See post-migration-validation.md
Step 7: Optimization and Cleanup
- Right-size resources based on actual usage
- Optimize costs using platform-native features
- Improve performance with platform capabilities
- Decommission source resources systematically
- Document final architecture
For cost optimization strategies: See cost-optimization-during-migration.md
Common Migration Scenarios
Cloud-to-Cloud Migration
Migrating between AWS, GCP, Azure, or moving from on-premise to cloud. Requires service mapping, network connectivity, and data transfer strategy.
Load: migration-types.md - Section 1: Cloud-to-Cloud Migration
Kubernetes Platform Migration
Moving clusters between EKS, GKE, AKS, or to self-managed Kubernetes. Involves workload migration, persistent storage, service meshes, and ingress controllers.
Load: migration-types.md - Section 2: Kubernetes Platform Migration
CI/CD Platform Changes
Transitioning between Jenkins, GitLab CI, GitHub Actions, CircleCI, or other platforms. Requires pipeline translation and secrets migration.
Load: migration-types.md - Section 3: CI/CD Platform Migration
Monitoring Stack Migration
Changing observability tools like Prometheus→Datadog, CloudWatch→New Relic. Includes metrics, logs, traces, and alerting.
Load: migration-types.md - Section 4: Monitoring Stack Migration
Container Registry Migration
Moving container images between Docker Hub, ECR, GCR, ACR, Harbor, or private registries.
Load: container-registry-migration.md
Network Infrastructure Migration
Transforming VPC configuration, VPN connections, DNS, load balancers, and CDN infrastructure.
Load: network-infrastructure-migration.md
Database Platform Migration
Migrating databases to different cloud providers or managed services.
Load: database-platform-migration.md
Migration Tools and Utilities
For platform-specific migration tools, automation scripts, and utilities:
Load: migration-tools.md
Includes cloud provider migration tools (AWS MGN, Azure Migrate, Google Cloud Migrate), Kubernetes tools (Velero, Kustomize), IaC tools (Terraformer, CDK), and data transfer utilities.
Reference Documentation
Migration Principles
Load: migration-principles.md Complete best practices, anti-patterns, and key considerations for any platform migration.
Risk Management
Load: risk-management.md Risk identification, mitigation strategies, contingency planning, and rollback procedures.
Post-Migration Validation
Load: post-migration-validation.md Validation checklists, testing procedures, performance benchmarking, and cutover verification.
Cost Optimization
Load: cost-optimization-during-migration.md Cost analysis, optimization strategies, and budget management during migration.
Output Formats
When generating migration artifacts, produce:
- Migration Plan: Detailed phases, timelines, dependencies, resources
- Architecture Diagrams: Source and target architecture with transition states
- Service Mapping: Complete mapping between source and target services
- Runbooks: Step-by-step execution procedures with rollback steps
- Validation Checklists: Acceptance criteria and testing procedures
- Risk Register: Identified risks, impact, probability, mitigation
- Cost Analysis: Comparative cost breakdown with optimization opportunities