Multi-Cloud Strategy: Avoiding Vendor Lock-in with Kubernetes

Vendor lock-in is one of the biggest concerns I hear from enterprise clients when planning their cloud strategy. After helping over 50+ organizations implement multi-cloud architectures, I've learned that the key isn't avoiding cloud-specific services entirely – it's about making strategic choices that preserve your flexibility while leveraging each cloud's strengths.

This comprehensive guide shows you how to build a robust multi-cloud strategy using Kubernetes as your abstraction layer, enabling you to deploy across AWS, Azure, and Google Cloud Platform while maintaining operational consistency and avoiding catastrophic vendor lock-in.

The Multi-Cloud Reality Check

Before diving into implementation, let's address the elephant in the room: pure cloud-agnostic architecture is often suboptimal. The most value in cloud computing comes from leveraging cloud-native services, not just running generic workloads on virtual machines.

The goal isn't to build the same thing everywhere – it's to architect for strategic flexibility while optimizing for each cloud's strengths.

Multi-Cloud Business Drivers

Organizations pursue multi-cloud strategies for several reasons:

- Risk Mitigation: Avoiding single points of failure and vendor dependencies - Regulatory Compliance: Data residency and sovereignty requirements - Cost Optimization: Leveraging competitive pricing and avoiding egress costs - Performance: Using edge locations and regional availability - Best-of-Breed Services: Choosing optimal services from each provider - Negotiating Power: Maintaining leverage in vendor relationships

Kubernetes as the Multi-Cloud Foundation

Kubernetes provides the perfect abstraction layer for multi-cloud deployments. Here's our proven architecture pattern:

multi-cloud-architecture.yaml - Kubernetes abstraction layer
apiVersion: v1
kind: ConfigMap
metadata:
  name: cloud-config
  namespace: kube-system
data:
  primary-cloud: "aws"
  regions: |
    aws:
      - us-east-1
      - eu-west-1
      - ap-southeast-1
    azure:
      - eastus
      - westeurope
      - southeastasia
    gcp:
      - us-central1
      - europe-west1
      - asia-southeast1
  deployment-strategy: "primary-secondary"
  failover-enabled: "true"
---
Cloud-agnostic storage class
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-ssd
  annotations:
    storageclass.kubernetes.io/is-default-class: "false"
provisioner: ${CLOUD_PROVISIONER}  # AWS: ebs.csi.aws.com, Azure: disk.csi.azure.com, GCP: pd.csi.storage.gke.io
parameters:
  type: ${DISK_TYPE}  # AWS: gp3, Azure: Premium_LRS, GCP: pd-ssd
  fsType: ext4
reclaimPolicy: Delete
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer
---
Multi-cloud ingress with provider abstraction
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: app-ingress
  annotations:
    # Cloud-agnostic annotations
    kubernetes.io/ingress.class: "nginx"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
    # Cloud-specific optimization (applied conditionally)
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/force-ssl-redirect: "true"
spec:
  tls:
  - hosts:
    - api.company.com
    secretName: api-tls
  rules:
  - host: api.company.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: api-service
            port:
              number: 80

Cloud Provider Abstraction Layer

Create a robust abstraction layer that handles cloud-specific differences:

#!/usr/bin/env python3
cloud-abstraction.py - Multi-cloud provider abstraction
import os
import json
import boto3
from azure.identity import DefaultAzureCredential
from azure.mgmt.compute import ComputeManagementClient
from google.cloud import compute_v1
from typing import Dict, List, Optional, Union
from dataclasses import dataclass
from abc import ABC, abstractmethod
@dataclass
class CloudResource:
    """Generic cloud resource representation"""
    id: str
    name: str
    type: str
    region: str
    status: str
    metadata: Dict[str, any]
@dataclass
class ComputeInstance:
    """Generic compute instance representation"""
    id: str
    name: str
    instance_type: str
    region: str
    status: str
    public_ip: Optional[str]
    private_ip: str
    tags: Dict[str, str]
class CloudProvider(ABC):
    """Abstract base class for cloud providers"""
    
    @abstractmethod
    def list_instances(self, region: str = None) -> List[ComputeInstance]:
        """List compute instances"""
        pass
    
    @abstractmethod
    def create_instance(self, config: Dict) -> ComputeInstance:
        """Create a new compute instance"""
        pass
    
    @abstractmethod
    def get_available_regions(self) -> List[str]:
        """Get list of available regions"""
        pass
    
    @abstractmethod
    def get_kubernetes_config(self, cluster_name: str, region: str) -> Dict:
        """Get Kubernetes cluster configuration"""
        pass
class AWSProvider(CloudProvider):
    """AWS implementation of cloud provider abstraction"""
    
    def __init__(self, profile: str = None):
        self.session = boto3.Session(profile_name=profile)
        self.ec2 = self.session.client('ec2')
        self.eks = self.session.client('eks')
    
    def list_instances(self, region: str = None) -> List[ComputeInstance]:
        """List EC2 instances"""
        if region:
            ec2_client = self.session.client('ec2', region_name=region)
        else:
            ec2_client = self.ec2
        
        response = ec2_client.describe_instances()
        instances = []
        
        for reservation in response['Reservations']:
            for instance in reservation['Instances']:
                # Extract tags
                tags = {tag['Key']: tag['Value'] for tag in instance.get('Tags', [])}
                
                instances.append(ComputeInstance(
                    id=instance['InstanceId'],
                    name=tags.get('Name', instance['InstanceId']),
                    instance_type=instance['InstanceType'],
                    region=instance['Placement']['AvailabilityZone'][:-1],
                    status=instance['State']['Name'],
                    public_ip=instance.get('PublicIpAddress'),
                    private_ip=instance.get('PrivateIpAddress', ''),
                    tags=tags
                ))
        
        return instances
    
    def create_instance(self, config: Dict) -> ComputeInstance:
        """Create EC2 instance"""
        response = self.ec2.run_instances(
            ImageId=config['image_id'],
            MinCount=1,
            MaxCount=1,
            InstanceType=config['instance_type'],
            KeyName=config.get('key_name'),
            SecurityGroupIds=config.get('security_groups', []),
            SubnetId=config.get('subnet_id'),
            TagSpecifications=[{
                'ResourceType': 'instance',
                'Tags': [{'Key': k, 'Value': v} for k, v in config.get('tags', {}).items()]
            }]
        )
        
        instance = response['Instances'][0]
        return ComputeInstance(
            id=instance['InstanceId'],
            name=config.get('tags', {}).get('Name', instance['InstanceId']),
            instance_type=instance['InstanceType'],
            region=instance['Placement']['AvailabilityZone'][:-1],
            status=instance['State']['Name'],
            public_ip=None,  # Will be assigned later
            private_ip=instance.get('PrivateIpAddress', ''),
            tags=config.get('tags', {})
        )
    
    def get_available_regions(self) -> List[str]:
        """Get AWS regions"""
        response = self.ec2.describe_regions()
        return [region['RegionName'] for region in response['Regions']]
    
    def get_kubernetes_config(self, cluster_name: str, region: str) -> Dict:
        """Get EKS cluster configuration"""
        eks_client = self.session.client('eks', region_name=region)
        
        cluster_info = eks_client.describe_cluster(name=cluster_name)
        cluster = cluster_info['cluster']
        
        return {
            'cluster_name': cluster_name,
            'endpoint': cluster['endpoint'],
            'ca_data': cluster['certificateAuthority']['data'],
            'region': region,
            'cloud_provider': 'aws',
            'node_groups': self._get_eks_node_groups(cluster_name, region),
            'addons': self._get_eks_addons(cluster_name, region)
        }
    
    def _get_eks_node_groups(self, cluster_name: str, region: str) -> List[Dict]:
        """Get EKS node groups"""
        eks_client = self.session.client('eks', region_name=region)
        
        node_groups = eks_client.list_nodegroups(clusterName=cluster_name)
        node_group_details = []
        
        for ng_name in node_groups['nodegroups']:
            ng_info = eks_client.describe_nodegroup(
                clusterName=cluster_name,
                nodegroupName=ng_name
            )
            node_group_details.append({
                'name': ng_name,
                'instance_types': ng_info['nodegroup']['instanceTypes'],
                'scaling_config': ng_info['nodegroup']['scalingConfig'],
                'status': ng_info['nodegroup']['status']
            })
        
        return node_group_details
    
    def _get_eks_addons(self, cluster_name: str, region: str) -> List[Dict]:
        """Get EKS addons"""
        eks_client = self.session.client('eks', region_name=region)
        
        try:
            addons = eks_client.list_addons(clusterName=cluster_name)
            addon_details = []
            
            for addon_name in addons['addons']:
                addon_info = eks_client.describe_addon(
                    clusterName=cluster_name,
                    addonName=addon_name
                )
                addon_details.append({
                    'name': addon_name,
                    'version': addon_info['addon']['addonVersion'],
                    'status': addon_info['addon']['status']
                })
            
            return addon_details
        except Exception:
            return []
class AzureProvider(CloudProvider):
    """Azure implementation of cloud provider abstraction"""
    
    def __init__(self, subscription_id: str = None):
        self.credential = DefaultAzureCredential()
        self.subscription_id = subscription_id or os.getenv('AZURE_SUBSCRIPTION_ID')
        self.compute_client = ComputeManagementClient(
            self.credential, 
            self.subscription_id
        )
    
    def list_instances(self, region: str = None) -> List[ComputeInstance]:
        """List Azure VMs"""
        instances = []
        
        for vm in self.compute_client.virtual_machines.list_all():
            # Filter by region if specified
            if region and vm.location != region:
                continue
            
            instances.append(ComputeInstance(
                id=vm.vm_id or vm.name,
                name=vm.name,
                instance_type=vm.hardware_profile.vm_size,
                region=vm.location,
                status=self._get_vm_status(vm.name, vm.id.split('/')[4]),  # Resource group
                public_ip=self._get_vm_public_ip(vm.name, vm.id.split('/')[4]),
                private_ip=self._get_vm_private_ip(vm.name, vm.id.split('/')[4]),
                tags=vm.tags or {}
            ))
        
        return instances
    
    def create_instance(self, config: Dict) -> ComputeInstance:
        """Create Azure VM"""
        # Implementation would go here
        # This is a simplified version
        raise NotImplementedError("Azure VM creation not implemented in this example")
    
    def get_available_regions(self) -> List[str]:
        """Get Azure regions"""
        from azure.mgmt.resource import ResourceManagementClient
        
        resource_client = ResourceManagementClient(
            self.credential, 
            self.subscription_id
        )
        
        locations = resource_client.subscriptions.list_locations(self.subscription_id)
        return [location.name for location in locations]
    
    def get_kubernetes_config(self, cluster_name: str, region: str) -> Dict:
        """Get AKS cluster configuration"""
        from azure.mgmt.containerservice import ContainerServiceClient
        
        container_client = ContainerServiceClient(
            self.credential,
            self.subscription_id
        )
        
        # Find the cluster (simplified - in reality you'd specify resource group)
        for resource_group in self._get_resource_groups():
            try:
                cluster = container_client.managed_clusters.get(
                    resource_group.name,
                    cluster_name
                )
                
                return {
                    'cluster_name': cluster_name,
                    'endpoint': cluster.fqdn,
                    'region': region,
                    'cloud_provider': 'azure',
                    'node_pools': [
                        {
                            'name': pool.name,
                            'vm_size': pool.vm_size,
                            'count': pool.count,
                            'status': pool.provisioning_state
                        }
                        for pool in cluster.agent_pool_profiles or []
                    ]
                }
            except Exception:
                continue
        
        raise ValueError(f"Cluster {cluster_name} not found")
    
    def _get_vm_status(self, vm_name: str, resource_group: str) -> str:
        """Get VM power state"""
        try:
            vm = self.compute_client.virtual_machines.get(
                resource_group, 
                vm_name,
                expand='instanceView'
            )
            
            for status in vm.instance_view.statuses:
                if status.code.startswith('PowerState/'):
                    return status.code.replace('PowerState/', '')
            
            return 'unknown'
        except Exception:
            return 'unknown'
    
    def _get_vm_public_ip(self, vm_name: str, resource_group: str) -> Optional[str]:
        """Get VM public IP address"""
        # Simplified implementation
        return None
    
    def _get_vm_private_ip(self, vm_name: str, resource_group: str) -> str:
        """Get VM private IP address"""
        # Simplified implementation
        return ""
    
    def _get_resource_groups(self):
        """Get resource groups"""
        from azure.mgmt.resource import ResourceManagementClient
        
        resource_client = ResourceManagementClient(
            self.credential,
            self.subscription_id
        )
        
        return resource_client.resource_groups.list()
class GCPProvider(CloudProvider):
    """Google Cloud implementation of cloud provider abstraction"""
    
    def __init__(self, project_id: str = None):
        self.project_id = project_id or os.getenv('GOOGLE_CLOUD_PROJECT')
        self.compute_client = compute_v1.InstancesClient()
        self.regions_client = compute_v1.RegionsClient()
    
    def list_instances(self, region: str = None) -> List[ComputeInstance]:
        """List GCP compute instances"""
        instances = []
        
        # List instances across all zones or specific region
        zones_to_check = []
        
        if region:
            # Get zones for specific region
            zones_request = compute_v1.ListZonesRequest(
                project=self.project_id,
                filter=f"region eq .*{region}"
            )
            zones = self.zones_client.list(request=zones_request)
            zones_to_check = [zone.name for zone in zones]
        else:
            # Get all zones
            zones_request = compute_v1.ListZonesRequest(project=self.project_id)
            zones = compute_v1.ZonesClient().list(request=zones_request)
            zones_to_check = [zone.name for zone in zones]
        
        for zone in zones_to_check:
            try:
                request = compute_v1.ListInstancesRequest(
                    project=self.project_id,
                    zone=zone
                )
                
                for instance in self.compute_client.list(request=request):
                    instances.append(ComputeInstance(
                        id=str(instance.id),
                        name=instance.name,
                        instance_type=instance.machine_type.split('/')[-1],
                        region=zone.rsplit('-', 1)[0],  # Extract region from zone
                        status=instance.status.lower(),
                        public_ip=self._get_instance_public_ip(instance),
                        private_ip=self._get_instance_private_ip(instance),
                        tags=dict(instance.labels) if instance.labels else {}
                    ))
            except Exception as e:
                print(f"Error listing instances in zone {zone}: {e}")
                continue
        
        return instances
    
    def create_instance(self, config: Dict) -> ComputeInstance:
        """Create GCP compute instance"""
        # Implementation would go here
        raise NotImplementedError("GCP instance creation not implemented in this example")
    
    def get_available_regions(self) -> List[str]:
        """Get GCP regions"""
        request = compute_v1.ListRegionsRequest(project=self.project_id)
        regions = self.regions_client.list(request=request)
        return [region.name for region in regions]
    
    def get_kubernetes_config(self, cluster_name: str, region: str) -> Dict:
        """Get GKE cluster configuration"""
        from google.cloud import container_v1
        
        container_client = container_v1.ClusterManagerClient()
        
        # Construct the parent path
        parent = f"projects/{self.project_id}/locations/{region}"
        
        try:
            cluster_path = f"{parent}/clusters/{cluster_name}"
            cluster = container_client.get_cluster(name=cluster_path)
            
            return {
                'cluster_name': cluster_name,
                'endpoint': f"https://{cluster.endpoint}",
                'ca_data': cluster.master_auth.cluster_ca_certificate,
                'region': region,
                'cloud_provider': 'gcp',
                'node_pools': [
                    {
                        'name': pool.name,
                        'machine_type': pool.config.machine_type,
                        'node_count': pool.initial_node_count,
                        'status': pool.status.name
                    }
                    for pool in cluster.node_pools
                ]
            }
        except Exception as e:
            raise ValueError(f"Error getting cluster {cluster_name}: {e}")
    
    def _get_instance_public_ip(self, instance) -> Optional[str]:
        """Extract public IP from instance"""
        try:
            for interface in instance.network_interfaces:
                for access_config in interface.access_configs:
                    if access_config.nat_ip:
                        return access_config.nat_ip
        except Exception:
            pass
        return None
    
    def _get_instance_private_ip(self, instance) -> str:
        """Extract private IP from instance"""
        try:
            if instance.network_interfaces:
                return instance.network_interfaces[0].network_ip or ""
        except Exception:
            pass
        return ""
class MultiCloudManager:
    """Unified multi-cloud management interface"""
    
    def __init__(self):
        self.providers = {}
        self._initialize_providers()
    
    def _initialize_providers(self):
        """Initialize available cloud providers"""
        try:
            self.providers['aws'] = AWSProvider()
        except Exception as e:
            print(f"AWS provider not available: {e}")
        
        try:
            self.providers['azure'] = AzureProvider()
        except Exception as e:
            print(f"Azure provider not available: {e}")
        
        try:
            self.providers['gcp'] = GCPProvider()
        except Exception as e:
            print(f"GCP provider not available: {e}")
    
    def get_all_instances(self) -> Dict[str, List[ComputeInstance]]:
        """Get instances from all available providers"""
        all_instances = {}
        
        for provider_name, provider in self.providers.items():
            try:
                instances = provider.list_instances()
                all_instances[provider_name] = instances
                print(f"Found {len(instances)} instances in {provider_name.upper()}")
            except Exception as e:
                print(f"Error listing instances from {provider_name}: {e}")
                all_instances[provider_name] = []
        
        return all_instances
    
    def get_kubernetes_clusters(self) -> Dict[str, Dict]:
        """Get Kubernetes cluster information from all providers"""
        clusters = {}
        
        # This would need cluster names and regions as input
        # Simplified for demonstration
        cluster_configs = {
            'aws': [('prod-cluster', 'us-east-1'), ('staging-cluster', 'us-west-2')],
            'azure': [('prod-cluster', 'eastus')],
            'gcp': [('prod-cluster', 'us-central1')]
        }
        
        for provider_name, provider in self.providers.items():
            clusters[provider_name] = {}
            
            if provider_name in cluster_configs:
                for cluster_name, region in cluster_configs[provider_name]:
                    try:
                        config = provider.get_kubernetes_config(cluster_name, region)
                        clusters[provider_name][cluster_name] = config
                    except Exception as e:
                        print(f"Error getting {cluster_name} from {provider_name}: {e}")
        
        return clusters
    
    def health_check(self) -> Dict[str, bool]:
        """Check health of all cloud providers"""
        health = {}
        
        for provider_name, provider in self.providers.items():
            try:
                # Simple health check - try to list regions
                regions = provider.get_available_regions()
                health[provider_name] = len(regions) > 0
            except Exception as e:
                print(f"Health check failed for {provider_name}: {e}")
                health[provider_name] = False
        
        return health
Example usage and testing
if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description='Multi-cloud management CLI')
    parser.add_argument('command', choices=['list-instances', 'health-check', 'clusters'],
                       help='Command to execute')
    parser.add_argument('--provider', choices=['aws', 'azure', 'gcp'],
                       help='Specific provider to target')
    
    args = parser.parse_args()
    
    manager = MultiCloudManager()
    
    if args.command == 'health-check':
        health = manager.health_check()
        print("\nProvider Health Status:")
        for provider, is_healthy in health.items():
            status = "✅ HEALTHY" if is_healthy else "❌ UNHEALTHY"
            print(f"  {provider.upper()}: {status}")
    
    elif args.command == 'list-instances':
        instances = manager.get_all_instances()
        
        for provider, instance_list in instances.items():
            if args.provider and provider != args.provider:
                continue
                
            print(f"\n{provider.upper()} Instances:")
            if not instance_list:
                print("  No instances found")
                continue
            
            for instance in instance_list:
                print(f"  {instance.name} ({instance.id})")
                print(f"    Type: {instance.instance_type}")
                print(f"    Region: {instance.region}")
                print(f"    Status: {instance.status}")
                print(f"    IPs: {instance.public_ip or 'N/A'} (public), {instance.private_ip} (private)")
                if instance.tags:
                    print(f"    Tags: {instance.tags}")
                print()
    
    elif args.command == 'clusters':
        clusters = manager.get_kubernetes_clusters()
        
        for provider, cluster_dict in clusters.items():
            if args.provider and provider != args.provider:
                continue
                
            print(f"\n{provider.upper()} Kubernetes Clusters:")
            if not cluster_dict:
                print("  No clusters found")
                continue
            
            for cluster_name, config in cluster_dict.items():
                print(f"  {cluster_name}")
                print(f"    Endpoint: {config['endpoint']}")
                print(f"    Region: {config['region']}")
                if 'node_pools' in config or 'node_groups' in config:
                    node_info = config.get('node_pools', config.get('node_groups', []))
                    print(f"    Node Pools/Groups: {len(node_info)}")
                print()

Unified CI/CD Pipeline for Multi-Cloud

Create a deployment pipeline that can target any cloud provider:

.gitlab-ci.yml - Multi-cloud deployment pipeline
stages:
  - validate
  - build
  - deploy-dev
  - test
  - deploy-staging
  - deploy-production
variables:
  DOCKER_DRIVER: overlay2
  DOCKER_TLS_CERTDIR: "/certs"
  
  # Multi-cloud configuration
  PRIMARY_CLOUD: "aws"
  SECONDARY_CLOUD: "azure"
  TERTIARY_CLOUD: "gcp"
  
  # Cloud-specific variables
  AWS_REGION: "us-east-1"
  AZURE_REGION: "eastus"
  GCP_REGION: "us-central1"
Validate cloud configurations
validate-cloud-configs:
  stage: validate
  image: alpine:latest
  before_script:
    - apk add --no-cache curl jq
  script:
    # Validate Kubernetes manifests for each cloud
    - |
      for cloud in aws azure gcp; do
        echo "Validating $cloud configuration..."
        
        # Substitute cloud-specific values
        envsubst < k8s/deployment.template.yaml > k8s/deployment-$cloud.yaml
        
        # Basic YAML validation
        python3 -c "import yaml; yaml.safe_load(open('k8s/deployment-$cloud.yaml'))"
        
        echo "✅ $cloud configuration valid"
      done
  artifacts:
    paths:
      - k8s/deployment-*.yaml
    expire_in: 1 hour
Build multi-arch container images
build-container:
  stage: build
  image: docker:20.10.16
  services:
    - docker:20.10.16-dind
  before_script:
    - echo $CI_REGISTRY_PASSWORD | docker login -u $CI_REGISTRY_USER --password-stdin $CI_REGISTRY
    - docker run --rm --privileged multiarch/qemu-user-static --reset -p yes
    - docker buildx create --use --name multi-arch-builder
  script:
    # Build multi-architecture images for different cloud architectures
    - |
      docker buildx build \
        --platform linux/amd64,linux/arm64 \
        --build-arg BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ') \
        --build-arg VCS_REF=$CI_COMMIT_SHA \
        --build-arg VERSION=$CI_COMMIT_TAG \
        -t $CI_REGISTRY_IMAGE:latest \
        -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA \
        --push .
  
Deploy to development (AWS primary)
deploy-dev-aws:
  stage: deploy-dev
  image: bitnami/kubectl:latest
  environment:
    name: development
    url: https://dev-api.company.com
  before_script:
    - echo "$AWS_KUBECONFIG" | base64 -d > kubeconfig
    - export KUBECONFIG=kubeconfig
    - kubectl config use-context dev-cluster
  script:
    - |
      # Apply cloud-specific configuration
      export CLOUD_PROVIDER="aws"
      export DISK_TYPE="gp3"
      export CLOUD_PROVISIONER="ebs.csi.aws.com"
      export INGRESS_CLASS="alb"
      
      # Deploy to AWS EKS
      envsubst < k8s/deployment.template.yaml | kubectl apply -f -
      kubectl set image deployment/api-server api-server=$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
      kubectl rollout status deployment/api-server --timeout=600s
      
      echo "✅ Deployed to AWS development cluster"
  only:
    - develop
    - merge_requests
Deploy to staging (multi-cloud)
deploy-staging-multicloud:
  stage: deploy-staging
  image: bitnami/kubectl:latest
  environment:
    name: staging
    url: https://staging-api.company.com
  parallel:
    matrix:
      - CLOUD: aws
        KUBECONFIG_VAR: AWS_STAGING_KUBECONFIG
        REGION: us-east-1
        DISK_TYPE: gp3
        PROVISIONER: ebs.csi.aws.com
        INGRESS_CLASS: alb
      - CLOUD: azure
        KUBECONFIG_VAR: AZURE_STAGING_KUBECONFIG
        REGION: eastus
        DISK_TYPE: Premium_LRS
        PROVISIONER: disk.csi.azure.com
        INGRESS_CLASS: nginx
      - CLOUD: gcp
        KUBECONFIG_VAR: GCP_STAGING_KUBECONFIG
        REGION: us-central1
        DISK_TYPE: pd-ssd
        PROVISIONER: pd.csi.storage.gke.io
        INGRESS_CLASS: gce
  before_script:
    - echo "${!KUBECONFIG_VAR}" | base64 -d > kubeconfig-$CLOUD
    - export KUBECONFIG=kubeconfig-$CLOUD
  script:
    - |
      # Apply cloud-specific configuration
      export CLOUD_PROVIDER=$CLOUD
      export CLOUD_PROVISIONER=$PROVISIONER
      
      # Deploy to specific cloud
      envsubst < k8s/deployment.template.yaml | kubectl apply -f -
      kubectl set image deployment/api-server api-server=$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
      kubectl rollout status deployment/api-server --timeout=600s
      
      # Verify deployment health
      kubectl get pods -l app=api-server
      kubectl get svc api-service
      
      echo "✅ Deployed to $CLOUD staging cluster"
  only:
    - master
Production deployment with traffic splitting
deploy-production:
  stage: deploy-production
  image: bitnami/kubectl:latest
  environment:
    name: production
    url: https://api.company.com
  when: manual
  script:
    - |
      # Deploy to primary cloud (AWS) first
      echo "Deploying to primary cloud (AWS)..."
      echo "$AWS_PROD_KUBECONFIG" | base64 -d > kubeconfig-aws
      export KUBECONFIG=kubeconfig-aws
      
      # Blue-green deployment on AWS
      export CLOUD_PROVIDER="aws"
      export DISK_TYPE="gp3"
      export CLOUD_PROVISIONER="ebs.csi.aws.com"
      
      envsubst < k8s/deployment.template.yaml | kubectl apply -f -
      kubectl set image deployment/api-server api-server=$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
      kubectl rollout status deployment/api-server --timeout=900s
      
      # Health check before proceeding
      if ! kubectl exec -it deploy/api-server -- curl -f http://localhost:8080/health; then
        echo "❌ Health check failed on AWS"
        exit 1
      fi
      
      echo "✅ AWS deployment successful"
      
      # Deploy to secondary cloud (Azure) with 30% traffic
      echo "Deploying to secondary cloud (Azure)..."
      echo "$AZURE_PROD_KUBECONFIG" | base64 -d > kubeconfig-azure
      export KUBECONFIG=kubeconfig-azure
      
      export CLOUD_PROVIDER="azure"
      export DISK_TYPE="Premium_LRS" 
      export CLOUD_PROVISIONER="disk.csi.azure.com"
      
      envsubst < k8s/deployment.template.yaml | kubectl apply -f -
      kubectl set image deployment/api-server api-server=$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
      kubectl rollout status deployment/api-server --timeout=900s
      
      # Configure traffic split (using Istio or similar)
      kubectl apply -f k8s/traffic-split-azure-30.yaml
      
      echo "✅ Multi-cloud production deployment complete"
      echo "Traffic split: 70% AWS, 30% Azure"
  only:
    - master
    - tags
Automated rollback capability
rollback-production:
  stage: deploy-production
  image: bitnami/kubectl:latest
  when: manual
  script:
    - |
      echo "Rolling back production deployment..."
      
      # Rollback on all clouds
      for cloud in aws azure; do
        echo "Rolling back $cloud..."
        
        kubeconfig_var="${cloud^^}_PROD_KUBECONFIG"
        echo "${!kubeconfig_var}" | base64 -d > kubeconfig-$cloud
        export KUBECONFIG=kubeconfig-$cloud
        
        # Rollback to previous version
        kubectl rollout undo deployment/api-server
        kubectl rollout status deployment/api-server --timeout=600s
        
        echo "✅ $cloud rollback complete"
      done
      
      echo "✅ Multi-cloud rollback complete"
  only:
    - master
    - tags

Network Architecture for Multi-Cloud

Implement secure, efficient networking across clouds:

#!/usr/bin/env python3
multi-cloud-networking.py - Cross-cloud network management
import json
import time
import subprocess
from typing import Dict, List, Optional
from dataclasses import dataclass
@dataclass
class NetworkPeering:
    """Network peering configuration"""
    name: str
    source_cloud: str
    target_cloud: str
    source_network: str
    target_network: str
    status: str
    bandwidth_gbps: float
    latency_ms: float
@dataclass
class NetworkRoute:
    """Network routing configuration"""
    destination_cidr: str
    next_hop: str
    priority: int
    cloud_provider: str
class MultiCloudNetworking:
    """Multi-cloud networking management"""
    
    def __init__(self):
        self.peering_connections = []
        self.routing_table = []
        self.vpn_connections = {}
        
    def setup_cross_cloud_connectivity(self) -> Dict:
        """Setup connectivity between cloud providers"""
        
        connectivity_map = {
            'aws-azure': self._setup_aws_azure_peering(),
            'aws-gcp': self._setup_aws_gcp_peering(),
            'azure-gcp': self._setup_azure_gcp_peering()
        }
        
        return connectivity_map
    
    def _setup_aws_azure_peering(self) -> Dict:
        """Setup AWS-Azure connectivity via VPN"""
        
        # AWS VPC configuration
        aws_config = {
            'vpc_cidr': '10.1.0.0/16',
            'public_subnets': ['10.1.1.0/24', '10.1.2.0/24'],
            'private_subnets': ['10.1.10.0/24', '10.1.20.0/24'],
            'vpn_gateway': {
                'type': 'ipsec.1',
                'routing': 'static',
                'tunnel_cidrs': ['169.254.21.0/30', '169.254.22.0/30']
            }
        }
        
        # Azure VNet configuration
        azure_config = {
            'vnet_cidr': '10.2.0.0/16',
            'public_subnets': ['10.2.1.0/24', '10.2.2.0/24'],
            'private_subnets': ['10.2.10.0/24', '10.2.20.0/24'],
            'vpn_gateway': {
                'type': 'RouteBased',
                'sku': 'VpnGw1',
                'tunnel_cidrs': ['169.254.21.0/30', '169.254.22.0/30']
            }
        }
        
        # Create Terraform configuration for AWS-Azure connectivity
        terraform_config = self._generate_aws_azure_terraform(aws_config, azure_config)
        
        return {
            'status': 'configured',
            'aws_config': aws_config,
            'azure_config': azure_config,
            'terraform': terraform_config,
            'estimated_latency_ms': 45,
            'bandwidth_gbps': 1.25
        }
    
    def _setup_aws_gcp_peering(self) -> Dict:
        """Setup AWS-GCP connectivity"""
        
        # Use Cloud Interconnect for high bandwidth
        aws_config = {
            'vpc_cidr': '10.1.0.0/16',
            'transit_gateway': True,
            'dedicated_connection': {
                'location': 'Equinix SV1',
                'bandwidth': '10Gbps',
                'vlan_id': 100
            }
        }
        
        gcp_config = {
            'vpc_cidr': '10.3.0.0/16',
            'cloud_router': True,
            'interconnect': {
                'type': 'DEDICATED',
                'location': 'Equinix-SV1',
                'bandwidth': '10Gbps',
                'vlan_id': 100
            }
        }
        
        return {
            'status': 'configured',
            'aws_config': aws_config,
            'gcp_config': gcp_config,
            'estimated_latency_ms': 25,
            'bandwidth_gbps': 10.0
        }
    
    def _setup_azure_gcp_peering(self) -> Dict:
        """Setup Azure-GCP connectivity"""
        
        azure_config = {
            'vnet_cidr': '10.2.0.0/16',
            'express_route': {
                'circuit_sku': 'Standard',
                'bandwidth': '1Gbps',
                'peering_location': 'Silicon Valley'
            }
        }
        
        gcp_config = {
            'vpc_cidr': '10.3.0.0/16',
            'partner_interconnect': {
                'type': 'PARTNER',
                'capacity': '1Gbps',
                'region': 'us-west1'
            }
        }
        
        return {
            'status': 'configured',
            'azure_config': azure_config,
            'gcp_config': gcp_config,
            'estimated_latency_ms': 35,
            'bandwidth_gbps': 1.0
        }
    
    def _generate_aws_azure_terraform(self, aws_config: Dict, azure_config: Dict) -> str:
        """Generate Terraform for AWS-Azure connectivity"""
        
        terraform_config = f"""
AWS VPC and VPN Gateway
provider "aws" {{
  region = "us-east-1"
}}
resource "aws_vpc" "main" {{
  cidr_block           = "{aws_config['vpc_cidr']}"
  enable_dns_hostnames = true
  enable_dns_support   = true
  
  tags = {{
    Name = "multi-cloud-vpc"
    Environment = "production"
  }}
}}
AWS Internet Gateway
resource "aws_internet_gateway" "main" {{
  vpc_id = aws_vpc.main.id
  
  tags = {{
    Name = "multi-cloud-igw"
  }}
}}
AWS VPN Gateway
resource "aws_vpn_gateway" "main" {{
  vpc_id = aws_vpc.main.id
  
  tags = {{
    Name = "multi-cloud-vpn-gw"
  }}
}}
Customer Gateway (Azure end)
resource "aws_customer_gateway" "azure" {{
  bgp_asn    = 65000
  ip_address = azurerm_public_ip.vpn_gateway.ip_address
  type       = "ipsec.1"
  
  tags = {{
    Name = "azure-customer-gateway"
  }}
}}
VPN Connection
resource "aws_vpn_connection" "azure" {{
  vpn_gateway_id      = aws_vpn_gateway.main.id
  customer_gateway_id = aws_customer_gateway.azure.id
  type                = "ipsec.1"
  static_routes_only  = true
  
  tags = {{
    Name = "aws-azure-vpn"
  }}
}}
VPN Connection Route
resource "aws_vpn_connection_route" "azure" {{
  vpn_connection_id      = aws_vpn_connection.azure.id
  destination_cidr_block = "{azure_config['vnet_cidr']}"
}}
Azure Provider
provider "azurerm" {{
  features {{}}
}}
Azure Resource Group
resource "azurerm_resource_group" "main" {{
  name     = "multi-cloud-rg"
  location = "East US"
}}
Azure Virtual Network
resource "azurerm_virtual_network" "main" {{
  name                = "multi-cloud-vnet"
  address_space       = ["{azure_config['vnet_cidr']}"]
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
}}
Azure Gateway Subnet
resource "azurerm_subnet" "gateway" {{
  name                 = "GatewaySubnet"
  resource_group_name  = azurerm_resource_group.main.name
  virtual_network_name = azurerm_virtual_network.main.name
  address_prefixes     = ["10.2.255.0/27"]
}}
Azure Public IP for VPN Gateway
resource "azurerm_public_ip" "vpn_gateway" {{
  name                = "multi-cloud-vpn-gateway-ip"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  allocation_method   = "Dynamic"
}}
Azure VPN Gateway
resource "azurerm_virtual_network_gateway" "main" {{
  name                = "multi-cloud-vpn-gateway"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  
  type     = "Vpn"
  vpn_type = "RouteBased"
  
  active_active = false
  enable_bgp    = false
  sku           = "VpnGw1"
  
  ip_configuration {{
    name                          = "vnetGatewayConfig"
    public_ip_address_id          = azurerm_public_ip.vpn_gateway.id
    private_ip_address_allocation = "Dynamic"
    subnet_id                     = azurerm_subnet.gateway.id
  }}
}}
Azure Local Network Gateway (AWS end)
resource "azurerm_local_network_gateway" "aws" {{
  name                = "aws-local-gateway"
  resource_group_name = azurerm_resource_group.main.name
  location            = azurerm_resource_group.main.location
  
  gateway_address = aws_vpn_connection.azure.tunnel1_address
  address_space   = ["{aws_config['vpc_cidr']}"]
}}
Azure VPN Connection
resource "azurerm_virtual_network_gateway_connection" "aws" {{
  name                = "azure-aws-connection"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  
  type                       = "IPSec"
  virtual_network_gateway_id = azurerm_virtual_network_gateway.main.id
  local_network_gateway_id   = azurerm_local_network_gateway.aws.id
  
  shared_key = aws_vpn_connection.azure.tunnel1_preshared_key
}}
Output connection information
output "aws_vpn_connection_id" {{
  value = aws_vpn_connection.azure.id
}}
output "azure_connection_id" {{
  value = azurerm_virtual_network_gateway_connection.aws.id
}}
output "tunnel_ips" {{
  value = {{
    tunnel1 = aws_vpn_connection.azure.tunnel1_address
    tunnel2 = aws_vpn_connection.azure.tunnel2_address
  }}
}}
"""
        
        return terraform_config
    
    def monitor_network_performance(self) -> Dict:
        """Monitor cross-cloud network performance"""
        
        performance_metrics = {}
        
        # Test connectivity between clouds
        test_endpoints = {
            'aws-azure': {
                'source': '10.1.10.10',
                'target': '10.2.10.10',
                'expected_latency_ms': 45
            },
            'aws-gcp': {
                'source': '10.1.10.10', 
                'target': '10.3.10.10',
                'expected_latency_ms': 25
            },
            'azure-gcp': {
                'source': '10.2.10.10',
                'target': '10.3.10.10', 
                'expected_latency_ms': 35
            }
        }
        
        for connection, config in test_endpoints.items():
            try:
                latency = self._measure_latency(config['source'], config['target'])
                bandwidth = self._measure_bandwidth(config['source'], config['target'])
                packet_loss = self._measure_packet_loss(config['source'], config['target'])
                
                performance_metrics[connection] = {
                    'latency_ms': latency,
                    'bandwidth_mbps': bandwidth,
                    'packet_loss_percent': packet_loss,
                    'status': 'healthy' if latency < config['expected_latency_ms'] * 1.5 else 'degraded'
                }
                
            except Exception as e:
                performance_metrics[connection] = {
                    'status': 'error',
                    'error': str(e)
                }
        
        return performance_metrics
    
    def _measure_latency(self, source: str, target: str) -> float:
        """Measure network latency between endpoints"""
        # Simplified implementation - in reality would use proper network tools
        try:
            result = subprocess.run(
                ['ping', '-c', '10', target],
                capture_output=True,
                text=True,
                timeout=30
            )
            
            # Parse ping output to get average latency
            output = result.stdout
            if 'avg' in output:
                # Extract average from: rtt min/avg/max/mdev = 1.234/5.678/9.012/1.234 ms
                avg_line = [line for line in output.split('\n') if 'avg' in line][0]
                avg_latency = float(avg_line.split('/')[5])  # Get avg value
                return avg_latency
            
            return 999.0  # High latency if parsing fails
            
        except Exception:
            return 999.0
    
    def _measure_bandwidth(self, source: str, target: str) -> float:
        """Measure bandwidth between endpoints"""
        # Simplified implementation - would use iperf3 or similar
        # For demo purposes, return simulated values
        return 850.0  # Mbps
    
    def _measure_packet_loss(self, source: str, target: str) -> float:
        """Measure packet loss between endpoints"""
        # Simplified implementation
        return 0.1  # 0.1% packet loss
    
    def optimize_routing(self) -> Dict:
        """Optimize routing across clouds"""
        
        optimization_results = {}
        
        # Analyze current routing performance
        current_performance = self.monitor_network_performance()
        
        # Identify optimization opportunities
        optimizations = []
        
        for connection, metrics in current_performance.items():
            if metrics.get('status') == 'degraded':
                if metrics.get('latency_ms', 0) > 100:
                    optimizations.append({
                        'connection': connection,
                        'issue': 'high_latency',
                        'recommendation': 'Consider dedicated connection or traffic engineering',
                        'priority': 'high'
                    })
                
                if metrics.get('packet_loss_percent', 0) > 1.0:
                    optimizations.append({
                        'connection': connection,
                        'issue': 'packet_loss',
                        'recommendation': 'Review QoS settings and connection reliability',
                        'priority': 'critical'
                    })
        
        # Apply automatic optimizations where possible
        applied_optimizations = []
        
        for opt in optimizations:
            if opt['issue'] == 'high_latency':
                # Enable traffic compression
                self._enable_traffic_compression(opt['connection'])
                applied_optimizations.append(f"Enabled compression for {opt['connection']}")
        
        optimization_results = {
            'identified_issues': len(optimizations),
            'optimizations_applied': applied_optimizations,
            'recommendations': optimizations,
            'next_review': time.time() + 3600  # Review in 1 hour
        }
        
        return optimization_results
    
    def _enable_traffic_compression(self, connection: str):
        """Enable traffic compression for a connection"""
        # Implementation would configure actual network devices
        print(f"Enabling traffic compression for {connection}")
Example usage
if __name__ == "__main__":
    networking = MultiCloudNetworking()
    
    print("Setting up multi-cloud connectivity...")
    connectivity = networking.setup_cross_cloud_connectivity()
    
    for connection, config in connectivity.items():
        print(f"\n{connection.upper()}:")
        print(f"  Status: {config['status']}")
        print(f"  Estimated Latency: {config['estimated_latency_ms']}ms")
        print(f"  Bandwidth: {config['bandwidth_gbps']}Gbps")
    
    print("\nMonitoring network performance...")
    performance = networking.monitor_network_performance()
    
    for connection, metrics in performance.items():
        print(f"\n{connection.upper()}:")
        if metrics.get('status') == 'error':
            print(f"  Status: ❌ Error - {metrics['error']}")
        else:
            print(f"  Status: {'✅' if metrics['status'] == 'healthy' else '⚠️'} {metrics['status']}")
            print(f"  Latency: {metrics.get('latency_ms', 'N/A')}ms")
            print(f"  Bandwidth: {metrics.get('bandwidth_mbps', 'N/A')}Mbps") 
            print(f"  Packet Loss: {metrics.get('packet_loss_percent', 'N/A')}%")
    
    print("\nOptimizing routing...")
    optimization = networking.optimize_routing()
    
    print(f"Issues identified: {optimization['identified_issues']}")
    if optimization['optimizations_applied']:
        print("Applied optimizations:")
        for opt in optimization['optimizations_applied']:
            print(f"  - {opt}")
    
    if optimization['recommendations']:
        print("Additional recommendations:")
        for rec in optimization['recommendations']:
            print(f"  - {rec['recommendation']} (Priority: {rec['priority']})")

Data Management Across Clouds

Implement consistent data management practices:

#!/usr/bin/env python3
multi-cloud-data.py - Cross-cloud data management
import json
import boto3
import asyncio
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
import hashlib
@dataclass 
class DataLocation:
    """Data location tracking"""
    cloud_provider: str
    region: str
    service: str  # S3, Blob Storage, Cloud Storage
    bucket_name: str
    path: str
    size_bytes: int
    last_modified: datetime
    encryption_status: str
@dataclass
class DataSyncJob:
    """Data synchronization job"""
    job_id: str
    source: DataLocation
    destination: DataLocation
    status: str
    progress_percent: float
    bytes_transferred: int
    start_time: datetime
    estimated_completion: Optional[datetime]
class MultiCloudDataManager:
    """Cross-cloud data management and synchronization"""
    
    def __init__(self):
        self.aws_s3 = boto3.client('s3')
        self.sync_jobs = {}
        self.data_catalog = {}
        
    def create_data_catalog(self) -> Dict:
        """Create comprehensive data catalog across clouds"""
        
        catalog = {
            'aws': self._catalog_aws_data(),
            'azure': self._catalog_azure_data(), 
            'gcp': self._catalog_gcp_data(),
            'metadata': {
                'total_objects': 0,
                'total_size_gb': 0,
                'last_updated': datetime.utcnow().isoformat()
            }
        }
        
        # Calculate totals
        total_objects = sum(len(cloud_data.get('objects', [])) for cloud_data in catalog.values() if isinstance(cloud_data, dict) and 'objects' in cloud_data)
        total_size = sum(
            sum(obj.get('size_bytes', 0) for obj in cloud_data.get('objects', []))
            for cloud_data in catalog.values() 
            if isinstance(cloud_data, dict) and 'objects' in cloud_data
        )
        
        catalog['metadata']['total_objects'] = total_objects
        catalog['metadata']['total_size_gb'] = round(total_size / (10243), 2)
        
        self.data_catalog = catalog
        return catalog
    
    def _catalog_aws_data(self) -> Dict:
        """Catalog AWS S3 data"""
        
        aws_data = {
            'buckets': [],
            'objects': [],
            'total_size_bytes': 0
        }
        
        try:
            # List all S3 buckets
            buckets_response = self.aws_s3.list_buckets()
            
            for bucket in buckets_response['Buckets']:
                bucket_name = bucket['Name']
                bucket_info = {
                    'name': bucket_name,
                    'creation_date': bucket['CreationDate'].isoformat(),
                    'region': self._get_bucket_region(bucket_name),
                    'objects': [],
                    'size_bytes': 0
                }
                
                try:
                    # List objects in bucket (limited to first 1000 for performance)
                    objects_response = self.aws_s3.list_objects_v2(
                        Bucket=bucket_name,
                        MaxKeys=1000
                    )
                    
                    if 'Contents' in objects_response:
                        for obj in objects_response['Contents']:
                            object_info = {
                                'key': obj['Key'],
                                'size_bytes': obj['Size'],
                                'last_modified': obj['LastModified'].isoformat(),
                                'etag': obj['ETag'].strip('"'),
                                'storage_class': obj.get('StorageClass', 'STANDARD')
                            }
                            
                            bucket_info['objects'].append(object_info)
                            bucket_info['size_bytes'] += obj['Size']
                            
                            # Add to global objects list
                            aws_data['objects'].append(DataLocation(
                                cloud_provider='aws',
                                region=bucket_info['region'],
                                service='s3',
                                bucket_name=bucket_name,
                                path=obj['Key'],
                                size_bytes=obj['Size'],
                                last_modified=obj['LastModified'],
                                encryption_status=self._check_s3_encryption(bucket_name, obj['Key'])
                            ))
                    
                    aws_data['total_size_bytes'] += bucket_info['size_bytes']
                    
                except Exception as e:
                    bucket_info['error'] = f"Unable to list objects: {str(e)}"
                
                aws_data['buckets'].append(bucket_info)
        
        except Exception as e:
            aws_data['error'] = f"Unable to access AWS S3: {str(e)}"
        
        return aws_data
    
    def _catalog_azure_data(self) -> Dict:
        """Catalog Azure Blob Storage data"""
        
        # Simplified implementation - would use Azure SDK
        azure_data = {
            'storage_accounts': [],
            'objects': [],
            'total_size_bytes': 0,
            'note': 'Requires Azure SDK implementation'
        }
        
        return azure_data
    
    def _catalog_gcp_data(self) -> Dict:
        """Catalog Google Cloud Storage data"""
        
        # Simplified implementation - would use GCP SDK
        gcp_data = {
            'buckets': [],
            'objects': [], 
            'total_size_bytes': 0,
            'note': 'Requires GCP SDK implementation'
        }
        
        return gcp_data
    
    def _get_bucket_region(self, bucket_name: str) -> str:
        """Get S3 bucket region"""
        try:
            response = self.aws_s3.get_bucket_location(Bucket=bucket_name)
            region = response.get('LocationConstraint')
            return region if region else 'us-east-1'  # Default region
        except Exception:
            return 'unknown'
    
    def _check_s3_encryption(self, bucket_name: str, object_key: str) -> str:
        """Check S3 object encryption status"""
        try:
            response = self.aws_s3.head_object(Bucket=bucket_name, Key=object_key)
            server_side_encryption = response.get('ServerSideEncryption', 'none')
            return server_side_encryption
        except Exception:
            return 'unknown'
    
    def setup_cross_cloud_replication(self, replication_config: Dict) -> Dict:
        """Setup data replication across clouds"""
        
        replication_jobs = []
        
        for config in replication_config.get('replications', []):
            job = DataSyncJob(
                job_id=f"sync-{hash(str(config))}",
                source=DataLocation(config['source']),
                destination=DataLocation(config['destination']),
                status='pending',
                progress_percent=0.0,
                bytes_transferred=0,
                start_time=datetime.utcnow(),
                estimated_completion=None
            )
            
            # Start replication job
            self._start_replication_job(job)
            replication_jobs.append(job)
        
        return {
            'jobs_created': len(replication_jobs),
            'jobs': [
                {
                    'job_id': job.job_id,
                    'source': f"{job.source.cloud_provider}:{job.source.bucket_name}/{job.source.path}",
                    'destination': f"{job.destination.cloud_provider}:{job.destination.bucket_name}/{job.destination.path}",
                    'status': job.status
                }
                for job in replication_jobs
            ]
        }
    
    def _start_replication_job(self, job: DataSyncJob):
        """Start a data replication job"""
        try:
            # Simplified implementation
            if job.source.cloud_provider == 'aws' and job.destination.cloud_provider == 'azure':
                self._replicate_aws_to_azure(job)
            elif job.source.cloud_provider == 'aws' and job.destination.cloud_provider == 'gcp':
                self._replicate_aws_to_gcp(job)
            # Add other combinations...
            
            job.status = 'running'
            self.sync_jobs[job.job_id] = job
            
        except Exception as e:
            job.status = 'failed'
            job.error = str(e)
    
    def _replicate_aws_to_azure(self, job: DataSyncJob):
        """Replicate data from AWS S3 to Azure Blob Storage"""
        
        # This would implement the actual replication logic
        # For now, simulate the process
        
        print(f"Starting replication: AWS S3 -> Azure Blob")
        print(f"Source: {job.source.bucket_name}/{job.source.path}")
        print(f"Destination: {job.destination.bucket_name}/{job.destination.path}")
        
        # In real implementation:
        # 1. Download from S3
        # 2. Upload to Azure Blob Storage
        # 3. Verify integrity
        # 4. Update job progress
        
        job.progress_percent = 100.0
        job.status = 'completed'
    
    def _replicate_aws_to_gcp(self, job: DataSyncJob):
        """Replicate data from AWS S3 to Google Cloud Storage"""
        
        print(f"Starting replication: AWS S3 -> Google Cloud Storage")
        print(f"Source: {job.source.bucket_name}/{job.source.path}")
        print(f"Destination: {job.destination.bucket_name}/{job.destination.path}")
        
        # Implementation would go here...
        job.progress_percent = 100.0
        job.status = 'completed'
    
    def monitor_data_consistency(self) -> Dict:
        """Monitor data consistency across clouds"""
        
        consistency_report = {
            'timestamp': datetime.utcnow().isoformat(),
            'checks_performed': 0,
            'inconsistencies_found': 0,
            'details': []
        }
        
        # Check for objects that should be replicated
        for job_id, job in self.sync_jobs.items():
            if job.status == 'completed':
                consistency_check = self._verify_replication_integrity(job)
                consistency_report['checks_performed'] += 1
                
                if not consistency_check['consistent']:
                    consistency_report['inconsistencies_found'] += 1
                    consistency_report['details'].append({
                        'job_id': job_id,
                        'issue': consistency_check['issue'],
                        'source_hash': consistency_check.get('source_hash'),
                        'destination_hash': consistency_check.get('destination_hash')
                    })
        
        return consistency_report
    
    def _verify_replication_integrity(self, job: DataSyncJob) -> Dict:
        """Verify integrity of replicated data"""
        
        try:
            # Get checksums from both source and destination
            source_hash = self._get_object_hash(job.source)
            destination_hash = self._get_object_hash(job.destination)
            
            if source_hash == destination_hash:
                return {
                    'consistent': True,
                    'source_hash': source_hash,
                    'destination_hash': destination_hash
                }
            else:
                return {
                    'consistent': False,
                    'issue': 'hash_mismatch',
                    'source_hash': source_hash,
                    'destination_hash': destination_hash
                }
        
        except Exception as e:
            return {
                'consistent': False,
                'issue': f'verification_error: {str(e)}'
            }
    
    def _get_object_hash(self, location: DataLocation) -> str:
        """Get hash of object at location"""
        
        if location.cloud_provider == 'aws':
            try:
                response = self.aws_s3.head_object(
                    Bucket=location.bucket_name,
                    Key=location.path
                )
                return response.get('ETag', '').strip('"')
            except Exception:
                return 'error'
        
        elif location.cloud_provider == 'azure':
            # Would implement Azure blob hash retrieval
            return 'azure_hash_placeholder'
        
        elif location.cloud_provider == 'gcp':
            # Would implement GCP object hash retrieval
            return 'gcp_hash_placeholder'
        
        return 'unknown'
    
    def optimize_data_placement(self) -> Dict:
        """Optimize data placement across clouds"""
        
        optimization_report = {
            'recommendations': [],
            'potential_savings_usd': 0,
            'performance_improvements': []
        }
        
        # Analyze current data catalog
        if not self.data_catalog:
            self.create_data_catalog()
        
        # Cost optimization recommendations
        for cloud_provider, data in self.data_catalog.items():
            if isinstance(data, dict) and 'objects' in data:
                
                for obj in data['objects']:
                    if isinstance(obj, DataLocation):
                        # Recommend moving cold data to cheaper storage
                        if self._is_cold_data(obj):
                            savings = self._calculate_storage_savings(obj)
                            optimization_report['recommendations'].append({
                                'type': 'storage_class_optimization',
                                'object': f"{obj.bucket_name}/{obj.path}",
                                'current_cloud': obj.cloud_provider,
                                'recommendation': 'Move to cold storage',
                                'potential_savings_usd_monthly': savings
                            })
                            optimization_report['potential_savings_usd'] += savings
                        
                        # Recommend geographic optimization
                        perf_improvement = self._analyze_geographic_placement(obj)
                        if perf_improvement:
                            optimization_report['performance_improvements'].append(perf_improvement)
        
        return optimization_report
    
    def _is_cold_data(self, obj: DataLocation) -> bool:
        """Determine if data is considered cold (rarely accessed)"""
        
        # Simple heuristic: data not modified in 90 days
        threshold_date = datetime.utcnow() - timedelta(days=90)
        return obj.last_modified < threshold_date
    
    def _calculate_storage_savings(self, obj: DataLocation) -> float:
        """Calculate potential storage cost savings"""
        
        # Simplified cost calculation (would use actual cloud pricing)
        storage_cost_per_gb = {
            'aws_standard': 0.023,  # S3 Standard
            'aws_ia': 0.0125,       # S3 IA
            'aws_glacier': 0.004,   # S3 Glacier
            'azure_hot': 0.024,     # Azure Hot
            'azure_cool': 0.015,    # Azure Cool
            'azure_archive': 0.002, # Azure Archive
            'gcp_standard': 0.020,  # GCP Standard
            'gcp_nearline': 0.010,  # GCP Nearline
            'gcp_coldline': 0.004   # GCP Coldline
        }
        
        current_cost_key = f"{obj.cloud_provider}_standard"
        cold_cost_key = f"{obj.cloud_provider}_glacier" if obj.cloud_provider == 'aws' else f"{obj.cloud_provider}_archive"
        
        current_cost = storage_cost_per_gb.get(current_cost_key, 0.025)
        cold_cost = storage_cost_per_gb.get(cold_cost_key, 0.005)
        
        size_gb = obj.size_bytes / (10243)
        monthly_savings = (current_cost - cold_cost) * size_gb
        
        return round(monthly_savings, 2)
    
    def _analyze_geographic_placement(self, obj: DataLocation) -> Optional[Dict]:
        """Analyze if data should be moved to different geographic location"""
        
        # Simplified analysis - would use actual access patterns
        access_regions = ['us-east-1', 'eu-west-1', 'ap-southeast-1']  # Most common access regions
        
        if obj.region not in access_regions:
            return {
                'object': f"{obj.bucket_name}/{obj.path}",
                'current_region': obj.region,
                'recommended_region': access_regions[0],  # Closest major region
                'expected_latency_improvement_ms': 50,
                'expected_cost_change_percent': -15
            }
        
        return None
Example usage and CLI
if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description='Multi-cloud data management')
    parser.add_argument('command', choices=['catalog', 'replicate', 'monitor', 'optimize'],
                       help='Command to execute')
    parser.add_argument('--config', help='Configuration file path')
    
    args = parser.parse_args()
    
    manager = MultiCloudDataManager()
    
    if args.command == 'catalog':
        print("Creating data catalog across clouds...")
        catalog = manager.create_data_catalog()
        
        print(f"\nData Catalog Summary:")
        print(f"Total Objects: {catalog['metadata']['total_objects']}")
        print(f"Total Size: {catalog['metadata']['total_size_gb']} GB")
        
        for cloud, data in catalog.items():
            if cloud != 'metadata' and isinstance(data, dict):
                if 'buckets' in data:
                    print(f"\n{cloud.upper()}:")
                    print(f"  Buckets: {len(data['buckets'])}")
                    print(f"  Objects: {len(data.get('objects', []))}")
                    print(f"  Size: {round(data.get('total_size_bytes', 0) / (10243), 2)} GB")
    
    elif args.command == 'monitor':
        print("Monitoring data consistency...")
        consistency = manager.monitor_data_consistency()
        
        print(f"\nConsistency Report:")
        print(f"Checks Performed: {consistency['checks_performed']}")
        print(f"Inconsistencies Found: {consistency['inconsistencies_found']}")
        
        if consistency['details']:
            print("\nInconsistencies:")
            for detail in consistency['details']:
                print(f"  Job {detail['job_id']}: {detail['issue']}")
    
    elif args.command == 'optimize':
        print("Analyzing data placement optimization...")
        opt_results = manager.optimize_data_placement()
        
        print("\nOptimization Report:")
        print(f"Recommendations: " + str(len(opt_results['recommendations'])))
        print(f"Potential Monthly Savings: \$" + str(opt_results['potential_savings_usd']))
        
        if opt_results['recommendations']:
            print("\nTop Recommendations:")
            for i, rec in enumerate(opt_results['recommendations'][:5], 1):
                print(f"  " + str(i) + ". " + rec['recommendation'])
                print(f"     Object: " + rec['object'])
                print(f"     Savings: \$" + str(rec['potential_savings_usd_monthly']) + "/month")
                print()
        
        if opt_results['performance_improvements']:
            print("Performance Improvements:")
            for improvement in opt_results['performance_improvements'][:3]:
                print(f"  - Move " + improvement['object'] + " to " + improvement['recommended_region'])
                print(f"    Expected latency improvement: " + str(improvement['expected_latency_improvement_ms']) + "ms")
                print()

Disaster Recovery Across Clouds

Implement robust disaster recovery strategies:

#!/bin/bash
multi-cloud-dr.sh - Disaster recovery orchestration
set -euo pipefail
Configuration
PRIMARY_CLOUD="${PRIMARY_CLOUD:-aws}"
SECONDARY_CLOUD="${SECONDARY_CLOUD:-azure}"
DR_NAMESPACE="${DR_NAMESPACE:-disaster-recovery}"
RTO_MINUTES="${RTO_MINUTES:-15}"  # Recovery Time Objective
RPO_MINUTES="${RPO_MINUTES:-5}"   # Recovery Point Objective
Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
log_info() {
    echo -e "${GREEN}[INFO]${NC} $1"
}
log_warn() {
    echo -e "${YELLOW}[WARN]${NC} $1"
}
log_error() {
    echo -e "${RED}[ERROR]${NC} $1"
}
log_debug() {
    echo -e "${BLUE}[DEBUG]${NC} $1"
}
Health check for primary cloud
check_primary_health() {
    local primary_cloud="$1"
    
    log_info "Checking health of primary cloud ($primary_cloud)..."
    
    case "$primary_cloud" in
        "aws")
            # Check AWS EKS cluster health
            if ! aws eks describe-cluster --name prod-cluster --region us-east-1 >/dev/null 2>&1; then
                log_error "AWS EKS cluster is not accessible"
                return 1
            fi
            
            # Check critical services
            kubectl config use-context aws-prod-cluster
            if ! kubectl get nodes --no-headers | grep -q "Ready"; then
                log_error "No ready nodes in AWS cluster"
                return 1
            fi
            
            # Check application pods
            local unhealthy_pods=$(kubectl get pods -n production --no-headers | grep -v "Running\|Completed" | wc -l)
            if [ "$unhealthy_pods" -gt 0 ]; then
                log_warn "$unhealthy_pods unhealthy pods detected in AWS"
                return 1
            fi
            ;;
            
        "azure")
            # Check Azure AKS cluster health
            if ! az aks show --name prod-cluster --resource-group prod-rg >/dev/null 2>&1; then
                log_error "Azure AKS cluster is not accessible"
                return 1
            fi
            
            kubectl config use-context azure-prod-cluster
            if ! kubectl get nodes --no-headers | grep -q "Ready"; then
                log_error "No ready nodes in Azure cluster"
                return 1
            fi
            ;;
            
        "gcp")
            # Check GKE cluster health
            if ! gcloud container clusters describe prod-cluster --zone us-central1-a >/dev/null 2>&1; then
                log_error "GCP GKE cluster is not accessible"
                return 1
            fi
            ;;
    esac
    
    log_info "Primary cloud ($primary_cloud) is healthy"
    return 0
}
Initiate disaster recovery failover
initiate_failover() {
    local primary="$1"
    local secondary="$2"
    local reason="${3:-manual_trigger}"
    
    log_info "🚨 INITIATING DISASTER RECOVERY FAILOVER"
    log_info "Primary: $primary -> Secondary: $secondary"
    log_info "Reason: $reason"
    log_info "RTO Target: $RTO_MINUTES minutes"
    
    # Record failover start time
    local failover_start=$(date +%s)
    
    # Step 1: Stop new traffic to primary
    log_info "Step 1: Stopping new traffic to primary cloud..."
    stop_primary_traffic "$primary"
    
    # Step 2: Ensure data consistency
    log_info "Step 2: Ensuring data consistency..."
    sync_data_to_secondary "$primary" "$secondary"
    
    # Step 3: Activate secondary cloud
    log_info "Step 3: Activating secondary cloud..."
    activate_secondary_cloud "$secondary"
    
    # Step 4: Update DNS and load balancers
    log_info "Step 4: Updating DNS to point to secondary cloud..."
    update_dns_to_secondary "$secondary"
    
    # Step 5: Verify secondary is operational
    log_info "Step 5: Verifying secondary cloud operation..."
    if verify_secondary_operation "$secondary"; then
        local failover_end=$(date +%s)
        local failover_duration=$(( (failover_end - failover_start) / 60 ))
        
        log_info "✅ Disaster recovery failover completed successfully"
        log_info "Failover duration: $failover_duration minutes (Target: $RTO_MINUTES minutes)"
        
        # Send notifications
        send_failover_notification "success" "$primary" "$secondary" "$failover_duration" "$reason"
        
        return 0
    else
        log_error "❌ Secondary cloud verification failed"
        log_error "Manual intervention required"
        
        send_failover_notification "failed" "$primary" "$secondary" "N/A" "$reason"
        return 1
    fi
}
Stop traffic to primary cloud
stop_primary_traffic() {
    local primary="$1"
    
    case "$primary" in
        "aws")
            # Update ALB target groups to drain connections
            log_debug "Draining AWS ALB target groups..."
            
            # Get ALB target group ARNs
            local target_groups=$(aws elbv2 describe-target-groups --query 'TargetGroups[?starts_with(TargetGroupName, prod-)].TargetGroupArn' --output text)
            
            for tg_arn in $target_groups; do
                log_debug "Draining target group: $tg_arn"
                # In reality, you'd modify the target group to remove healthy targets
                # aws elbv2 modify-target-group --target-group-arn $tg_arn --health-check-enabled false
            done
            ;;
            
        "azure")
            # Update Azure Load Balancer
            log_debug "Updating Azure Load Balancer rules..."
            # az network lb rule update --resource-group prod-rg --lb-name prod-lb --name http-rule --backend-pool-name empty-pool
            ;;
            
        "gcp")
            # Update GCP Load Balancer
            log_debug "Updating GCP Load Balancer backend services..."
            # gcloud compute backend-services update prod-backend --global --no-backends
            ;;
    esac
    
    log_info "Traffic stopped to primary cloud ($primary)"
}
Synchronize data to secondary cloud
sync_data_to_secondary() {
    local primary="$1"
    local secondary="$2"
    
    log_info "Synchronizing critical data from $primary to $secondary..."
    
    # Database synchronization
    case "$primary-$secondary" in
        "aws-azure")
            log_debug "Syncing AWS RDS to Azure Database..."
            # Create final backup and restore to Azure
            python3 /scripts/sync-aws-azure-db.py --final-sync
            ;;
            
        "aws-gcp")
            log_debug "Syncing AWS RDS to Cloud SQL..."
            python3 /scripts/sync-aws-gcp-db.py --final-sync
            ;;
            
        "azure-aws")
            log_debug "Syncing Azure Database to AWS RDS..."
            python3 /scripts/sync-azure-aws-db.py --final-sync
            ;;
    esac
    
    # Application state synchronization
    log_debug "Syncing application state and sessions..."
    
    # Redis/cache synchronization
    kubectl config use-context "${primary}-prod-cluster"
    kubectl exec -n production deploy/redis -- redis-cli BGSAVE
    
    # Wait for backup to complete
    sleep 10
    
    # Copy Redis dump to secondary cloud
    kubectl cp production/redis-0:/data/dump.rdb /tmp/redis-backup.rdb
    
    kubectl config use-context "${secondary}-prod-cluster"
    kubectl cp /tmp/redis-backup.rdb production/redis-0:/data/dump.rdb
    kubectl exec -n production deploy/redis -- redis-cli DEBUG RESTART
    
    log_info "Data synchronization completed"
}
Activate secondary cloud
activate_secondary_cloud() {
    local secondary="$1"
    
    log_info "Activating secondary cloud ($secondary)..."
    
    kubectl config use-context "${secondary}-prod-cluster"
    
    # Scale up applications in secondary cloud
    log_debug "Scaling up applications in secondary cloud..."
    
    # Scale critical services
    kubectl scale deployment api-server --replicas=5 -n production
    kubectl scale deployment worker --replicas=3 -n production
    kubectl scale deployment frontend --replicas=3 -n production
    
    # Wait for pods to be ready
    log_debug "Waiting for pods to be ready..."
    kubectl wait --for=condition=ready pod -l app=api-server -n production --timeout=600s
    kubectl wait --for=condition=ready pod -l app=worker -n production --timeout=600s
    kubectl wait --for=condition=ready pod -l app=frontend -n production --timeout=600s
    
    # Update configuration for DR mode
    log_debug "Updating configuration for disaster recovery mode..."
    kubectl patch configmap app-config -n production --patch '{
        "data": {
            "mode": "disaster-recovery",
            "primary-cloud": "false",
            "dr-activated-timestamp": "'"$(date -u +%Y-%m-%dT%H:%M:%SZ)"'"
        }
    }'
    
    # Restart pods to pick up new configuration
    kubectl rollout restart deployment/api-server -n production
    kubectl rollout restart deployment/worker -n production
    
    log_info "Secondary cloud ($secondary) activated"
}
Update DNS to point to secondary cloud
update_dns_to_secondary() {
    local secondary="$1"
    
    log_info "Updating DNS records to point to secondary cloud..."
    
    # Get secondary cloud load balancer IP
    kubectl config use-context "${secondary}-prod-cluster"
    local secondary_lb_ip=$(kubectl get service api-service -n production -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
    
    if [ -z "$secondary_lb_ip" ]; then
        # Try hostname for AWS ELB
        secondary_lb_ip=$(kubectl get service api-service -n production -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
    fi
    
    if [ -z "$secondary_lb_ip" ]; then
        log_error "Unable to get secondary cloud load balancer IP/hostname"
        return 1
    fi
    
    log_debug "Secondary load balancer: $secondary_lb_ip"
    
    # Update DNS records (example using Route53)
    case "$secondary" in
        "aws")
            # Update Route53 record
            aws route53 change-resource-record-sets --hosted-zone-id Z123456789 --change-batch '{
                "Changes": [{
                    "Action": "UPSERT",
                    "ResourceRecordSet": {
                        "Name": "api.company.com",
                        "Type": "A",
                        "TTL": 60,
                        "ResourceRecords": [{"Value": "'"$secondary_lb_ip"'"}]
                    }
                }]
            }'
            ;;
            
        "azure")
            # Update Azure DNS
            az network dns record-set a add-record --resource-group dns-rg --zone-name company.com --record-set-name api --ipv4-address "$secondary_lb_ip"
            ;;
            
        "gcp")
            # Update Cloud DNS
            gcloud dns record-sets transaction start --zone=company-com
            gcloud dns record-sets transaction add --zone=company-com --name=api.company.com. --ttl=60 --type=A "$secondary_lb_ip"
            gcloud dns record-sets transaction execute --zone=company-com
            ;;
    esac
    
    log_info "DNS updated to point to secondary cloud"
}
Verify secondary cloud operation
verify_secondary_operation() {
    local secondary="$1"
    
    log_info "Verifying secondary cloud operation..."
    
    kubectl config use-context "${secondary}-prod-cluster"
    
    # Get service endpoint
    local service_ip=$(kubectl get service api-service -n production -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
    if [ -z "$service_ip" ]; then
        service_ip=$(kubectl get service api-service -n production -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
    fi
    
    if [ -z "$service_ip" ]; then
        log_error "Unable to get service endpoint for verification"
        return 1
    fi
    
    # Health check
    log_debug "Testing health endpoint..."
    if ! curl -f -s "http://$service_ip/health" >/dev/null; then
        log_error "Health check failed"
        return 1
    fi
    
    # API functionality test
    log_debug "Testing API functionality..."
    local api_response=$(curl -s "http://$service_ip/api/status")
    if ! echo "$api_response" | jq -e '.status == "healthy"' >/dev/null 2>&1; then
        log_error "API functionality test failed"
        return 1
    fi
    
    # Database connectivity test
    log_debug "Testing database connectivity..."
    if ! kubectl exec -n production deploy/api-server -- /app/health-check --database >/dev/null 2>&1; then
        log_error "Database connectivity test failed"
        return 1
    fi
    
    log_info "✅ Secondary cloud operation verified"
    return 0
}
Send failover notifications
send_failover_notification() {
    local status="$1"
    local primary="$2"  
    local secondary="$3"
    local duration="$4"
    local reason="$5"
    
    local webhook_url="${SLACK_WEBHOOK_URL:-}"
    
    if [ -z "$webhook_url" ]; then
        log_warn "No notification webhook configured"
        return 0
    fi
    
    local color="good"
    local emoji="✅"
    if [ "$status" = "failed" ]; then
        color="danger"
        emoji="❌"
    fi
    
    local message="{
        "attachments": [{
            "color": "$color",
            "title": "$emoji Disaster Recovery Failover - $status",
            "fields": [
                {"title": "Primary Cloud", "value": "$primary", "short": true},
                {"title": "Secondary Cloud", "value": "$secondary", "short": true},
                {"title": "Duration", "value": "$duration minutes", "short": true},
                {"title": "Reason", "value": "$reason", "short": true},
                {"title": "Timestamp", "value": "$(date -u)", "short": false}
            ]
        }]
    }"
    
    curl -X POST -H 'Content-type: application/json' \
        --data "$message" \
        "$webhook_url"
}
Automated monitoring and triggering
monitor_and_trigger() {
    log_info "Starting automated DR monitoring..."
    
    while true; do
        if ! check_primary_health "$PRIMARY_CLOUD"; then
            log_warn "Primary cloud health check failed"
            
            # Wait and check again to avoid false positives
            sleep 30
            
            if ! check_primary_health "$PRIMARY_CLOUD"; then
                log_error "Primary cloud confirmed unhealthy - triggering failover"
                initiate_failover "$PRIMARY_CLOUD" "$SECONDARY_CLOUD" "automated_health_check_failure"
                break
            else
                log_info "Primary cloud recovered - false alarm"
            fi
        else
            log_debug "Primary cloud healthy"
        fi
        
        sleep 60  # Check every minute
    done
}
Failback to primary cloud
initiate_failback() {
    local current_primary="$1"
    local original_primary="$2"
    
    log_info "🔄 INITIATING FAILBACK TO ORIGINAL PRIMARY"
    log_info "Current Primary: $current_primary -> Original Primary: $original_primary"
    
    # Verify original primary is healthy
    if ! check_primary_health "$original_primary"; then
        log_error "Original primary cloud is not healthy - cannot failback"
        return 1
    fi
    
    # Sync data back to original primary
    sync_data_to_secondary "$current_primary" "$original_primary"
    
    # Activate original primary
    activate_secondary_cloud "$original_primary"
    
    # Update DNS back to original primary
    update_dns_to_secondary "$original_primary"
    
    # Verify operation
    if verify_secondary_operation "$original_primary"; then
        log_info "✅ Failback to original primary completed successfully"
        
        # Scale down the secondary (former primary)
        kubectl config use-context "${current_primary}-prod-cluster"
        kubectl scale deployment api-server --replicas=1 -n production
        kubectl scale deployment worker --replicas=1 -n production
        kubectl scale deployment frontend --replicas=1 -n production
        
        return 0
    else
        log_error "❌ Failback verification failed"
        return 1
    fi
}
Test disaster recovery plan
test_dr_plan() {
    log_info "🧪 TESTING DISASTER RECOVERY PLAN"
    
    # Create test namespace
    kubectl create namespace dr-test --dry-run=client -o yaml | kubectl apply -f -
    
    # Deploy test application to secondary cloud
    kubectl config use-context "${SECONDARY_CLOUD}-prod-cluster"
    
    # Apply test deployment
    cat </dev/null; then
        log_info "✅ DR test successful - secondary cloud is operational"
    else
        log_error "❌ DR test failed - secondary cloud has issues"
    fi
    
    # Cleanup test resources
    kubectl delete namespace dr-test
    
    log_info "DR plan test completed"
}
Main function
main() {
    case "${1:-}" in
        "monitor")
            monitor_and_trigger
            ;;
        "failover")
            initiate_failover "${2:-$PRIMARY_CLOUD}" "${3:-$SECONDARY_CLOUD}" "${4:-manual_trigger}"
            ;;
        "failback")
            initiate_failback "${2:-$SECONDARY_CLOUD}" "${3:-$PRIMARY_CLOUD}"
            ;;
        "test")
            test_dr_plan
            ;;
        "health-check")
            check_primary_health "${2:-$PRIMARY_CLOUD}"
            ;;
        *)
            echo "Usage: $0 {monitor|failover|failback|test|health-check} [args...]"
            echo ""
            echo "Commands:"
            echo "  monitor                                - Start automated DR monitoring"
            echo "  failover [primary] [secondary] [reason] - Initiate disaster recovery failover"
            echo "  failback [current] [original]         - Failback to original primary cloud"
            echo "  test                                   - Test disaster recovery plan"
            echo "  health-check [cloud]                   - Check health of specified cloud"
            echo ""
            echo "Environment Variables:"
            echo "  PRIMARY_CLOUD    - Primary cloud provider (default: aws)"
            echo "  SECONDARY_CLOUD  - Secondary cloud provider (default: azure)"
            echo "  RTO_MINUTES      - Recovery Time Objective (default: 15)"
            echo "  RPO_MINUTES      - Recovery Point Objective (default: 5)"
            echo "  SLACK_WEBHOOK_URL - Slack webhook for notifications"
            exit 1
            ;;
    esac
}
main "$@"

Conclusion

Building a successful multi-cloud strategy isn't about avoiding all cloud-specific services – it's about making informed architectural decisions that preserve your strategic flexibility while leveraging each cloud's strengths.

Key principles for multi-cloud success:

1. Use Kubernetes as Your Abstraction Layer: It provides the perfect balance of portability and cloud integration 2. Design for Operational Consistency: Unified monitoring, logging, and deployment processes across clouds 3. Implement Strategic Data Management: Know where your data lives and how to move it when needed 4. Plan for Disaster Recovery: Test your cross-cloud failover capabilities regularly 5. Optimize Costs Continuously: Leverage each cloud's pricing advantages for different workloads

The architecture patterns and tooling shown in this guide provide the foundation for a robust multi-cloud strategy that gives you:

- Vendor Independence: Never be locked into a single provider's ecosystem - Risk Mitigation: Distribute your infrastructure risk across multiple providers - Cost Optimization: Use the most cost-effective cloud for each workload - Performance Optimization: Leverage regional advantages and specialized services - Negotiating Power: Maintain leverage in vendor relationships

Remember: The goal isn't to build identical systems everywhere, but to architect for strategic optionality while delivering maximum business value.

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Need help implementing a multi-cloud strategy for your organization? Contact our cloud architects for guidance on Kubernetes-based multi-cloud design, vendor selection, and migration planning.