CloudMigrationAWSAzureGCP

Cloud-to-Cloud Migration Strategy

Step-by-step guide for migrating workloads between AWS, Azure, and GCP

October 20, 202415 min read

Planning the Migration

Cloud-to-cloud migration is one of the most complex undertakings in modern infrastructure. Whether moving from AWS to Azure, GCP, or establishing a multi-cloud presence, success depends on meticulous planning. Start with a comprehensive assessment: - Inventory all services, dependencies, and data flows - Map source cloud services to destination equivalents - Identify services with no direct equivalent (requiring re-architecture) - Assess data gravity — large datasets are expensive and slow to move - Establish success criteria and rollback thresholds - Build a RACI matrix for every phase of the migration The 6 R's framework helps categorize each workload: Rehost (lift-and-shift), Replatform, Refactor, Repurchase, Retire, or Retain.

1# Terraform multi-cloud provider configuration
2terraform {
3  required_providers {
4    aws = {
5      source  = "hashicorp/aws"
6      version = "~> 5.0"
7    }
8    azurerm = {
9      source  = "hashicorp/azurerm"
10      version = "~> 3.0"
11    }
12    google = {
13      source  = "hashicorp/google"
14      version = "~> 5.0"
15    }
16  }
17
18  backend "s3" {
19    bucket         = "terraform-state-migration"
20    key            = "multi-cloud/terraform.tfstate"
21    region         = "us-east-1"
22    encrypt        = true
23    dynamodb_table = "terraform-locks"
24  }
25}
26
27# Source: AWS VPC
28resource "aws_vpc" "source" {
29  cidr_block = "10.0.0.0/16"
30  tags = { Name = "source-vpc", Phase = "migration" }
31}
32
33# Destination: Azure VNet
34resource "azurerm_virtual_network" "destination" {
35  name                = "dest-vnet"
36  location            = "eastus"
37  resource_group_name = azurerm_resource_group.migration.name
38  address_space       = ["10.1.0.0/16"]
39}
40
41# VPN connection between clouds
42resource "aws_vpn_gateway" "cross_cloud" {
43  vpc_id = aws_vpc.source.id
44  tags   = { Name = "aws-to-azure-vpn" }
45}

Data Migration Strategy

Data migration is often the most challenging and risky phase. The strategy depends on data volume, acceptable downtime, and consistency requirements. Key approaches: - Online Migration: Use change data capture (CDC) to replicate data in real-time while the source remains active. Ideal for zero-downtime requirements. - Offline Migration: Perform a bulk data transfer during a maintenance window. Simpler but requires downtime. - Hybrid: Bulk transfer followed by CDC to sync delta changes. For databases, consider using native migration tools: - AWS DMS (Database Migration Service) for relational databases - Azure Database Migration Service for Azure targets - Cloud SQL migration for GCP destinations For object storage, use cross-cloud transfer services or tools like rclone for efficient parallel transfers.

1#!/bin/bash
2# Cross-cloud data migration script using rclone
3
4# Configure remotes (one-time setup)
5# rclone config (interactive setup for AWS S3 and Azure Blob)
6
7SOURCE="aws-s3:production-data"
8DEST="azure-blob:production-data"
9LOG_FILE="/var/log/migration/$(date +%Y%m%d).log"
10
11echo "Starting cross-cloud data migration..."
12
13# Sync with checksum verification and parallel transfers
14rclone sync "$SOURCE" "$DEST" \
15  --transfers 32 \
16  --checkers 16 \
17  --checksum \
18  --progress \
19  --log-file "$LOG_FILE" \
20  --log-level INFO \
21  --stats 30s \
22  --retries 3 \
23  --low-level-retries 10
24
25# Verify transfer integrity
26echo "Verifying data integrity..."
27rclone check "$SOURCE" "$DEST" --one-way \
28  --log-file "$LOG_FILE"
29
30SOURCE_COUNT=$(rclone size "$SOURCE" --json | jq '.count')
31DEST_COUNT=$(rclone size "$DEST" --json | jq '.count')
32
33echo "Source objects: $SOURCE_COUNT"
34echo "Destination objects: $DEST_COUNT"
35
36if [ "$SOURCE_COUNT" = "$DEST_COUNT" ]; then
37  echo "Migration verified successfully!"
38else
39  echo "WARNING: Object count mismatch!"
40  exit 1
41fi

Application Migration & Testing

Application migration requires a systematic approach to ensure functionality and performance parity with the source environment. The process typically follows these steps: 1. Containerize applications if not already running in containers — Docker provides cloud-agnostic portability 2. Deploy Kubernetes clusters on the target cloud (EKS/AKS/GKE) 3. Set up CI/CD pipelines for the new environment 4. Configure networking, DNS, and SSL certificates 5. Deploy application workloads with Helm charts 6. Run comprehensive test suites against the new environment 7. Perform load testing to validate performance parity 8. Execute canary deployments to gradually shift traffic Testing checklist: - Functional testing: all APIs and workflows operate correctly - Performance testing: response times match or improve baseline - Integration testing: all service-to-service communication works - Security testing: network policies and access controls are correct - Disaster recovery testing: failover and backup procedures work

1# GitHub Actions CI/CD for multi-cloud deployment
2name: Multi-Cloud Deploy
3on:
4  push:
5    branches: [main]
6
7jobs:
8  build:
9    runs-on: ubuntu-latest
10    steps:
11      - uses: actions/checkout@v4
12
13      - name: Build and push Docker image
14        run: |
15          docker build -t app:${{ github.sha }} .
16          # Push to both registries
17          docker tag app:${{ github.sha }} ${{ secrets.AWS_ECR }}/app:${{ github.sha }}
18          docker tag app:${{ github.sha }} ${{ secrets.ACR_URL }}/app:${{ github.sha }}
19          docker push ${{ secrets.AWS_ECR }}/app:${{ github.sha }}
20          docker push ${{ secrets.ACR_URL }}/app:${{ github.sha }}
21
22  deploy-aws:
23    needs: build
24    runs-on: ubuntu-latest
25    steps:
26      - name: Deploy to EKS
27        run: |
28          aws eks update-kubeconfig --name prod-cluster --region us-east-1
29          helm upgrade --install app ./charts/app \\
30            --set image.tag=${{ github.sha }} \\
31            --set cloud=aws \\
32            -f values-aws.yaml
33
34  deploy-azure:
35    needs: build
36    runs-on: ubuntu-latest
37    steps:
38      - name: Deploy to AKS
39        run: |
40          az aks get-credentials --resource-group prod --name aks-cluster
41          helm upgrade --install app ./charts/app \\
42            --set image.tag=${{ github.sha }} \\
43            --set cloud=azure \\
44            -f values-azure.yaml
45
46  verify:
47    needs: [deploy-aws, deploy-azure]
48    runs-on: ubuntu-latest
49    steps:
50      - name: Run smoke tests
51        run: |
52          curl -f https://aws.example.com/health || exit 1
53          curl -f https://azure.example.com/health || exit 1
54          echo "Both environments healthy!"

Challenges & Lessons Learned

Every cloud migration surfaces unexpected challenges. Here are the most common pitfalls and how to address them: Networking Complexity: Each cloud has its own networking model. VPN interconnects between clouds add latency — measure and account for this. Use tools like iperf3 and traceroute to establish baselines. Service Parity Gaps: Not all services have direct equivalents. For example, AWS Lambda, Azure Functions, and Cloud Functions have different runtime limits, cold start behaviors, and integration patterns. Abstract these behind cloud-agnostic interfaces. Cost Management: Different pricing models can lead to surprises. AWS charges for NAT gateway bandwidth, Azure for inter-region data transfer, GCP for egress. Set up billing alerts and use tools like Infracost to predict costs before deploying. IAM & Security: Each cloud has its own identity system. Use federated identity where possible, and maintain a centralized secrets management solution (HashiCorp Vault works across all clouds). DNS & Certificate Management: Plan DNS cutover carefully. Use short TTLs before migration, and ensure TLS certificates are provisioned on the target before switching traffic.