Cloud Devops Expert

Core Services:

• Compute: EC2, Lambda (serverless), ECS/EKS (containers), Fargate
• Storage: S3 (object), EBS (block), EFS (file system)
• Database: RDS (relational), DynamoDB (NoSQL), Aurora (MySQL/PostgreSQL)
• Networking: VPC, ALB/NLB, CloudFront (CDN), Route 53 (DNS)
• Monitoring: CloudWatch (metrics, logs, alarms)

Best Practices:

• Use AWS Organizations for multi-account management
• Implement least privilege with IAM roles and policies
• Enable CloudTrail for audit logging
• Use AWS Config for compliance and resource tracking
• Tag all resources for cost allocation and management

GCP (Google Cloud Platform) Patterns

Core Services:

• Compute: Compute Engine (VMs), Cloud Functions (serverless), GKE (Kubernetes)
• Storage: Cloud Storage (object), Persistent Disk (block)
• Database: Cloud SQL, Cloud Spanner, Firestore
• Networking: VPC, Cloud Load Balancing, Cloud CDN
• Monitoring: Cloud Monitoring, Cloud Logging

Best Practices:

• Use Google Cloud Identity for centralized identity management
• Implement VPC Service Controls for security perimeters
• Enable Cloud Audit Logs for compliance
• Use labels for resource organization and billing

Azure Patterns

Core Services:

• Compute: Virtual Machines, Azure Functions, AKS (Kubernetes), Container Instances
• Storage: Blob Storage, Azure Files, Managed Disks
• Database: Azure SQL, Cosmos DB (NoSQL), PostgreSQL/MySQL
• Networking: Virtual Network, Application Gateway, Front Door (CDN)
• Monitoring: Azure Monitor, Log Analytics

Best Practices:

• Use Azure AD for identity and access management
• Implement Azure Policy for governance
• Enable Azure Security Center for threat protection
• Use resource groups for logical organization

Terraform Best Practices

Project Structure:

terraform/
├── environments/
│   ├── dev/
│   │   ├── main.tf
│   │   ├── variables.tf
│   │   └── terraform.tfvars
│   ├── staging/
│   └── prod/
├── modules/
│   ├── vpc/
│   ├── eks/
│   └── rds/
└── global/
    └── backend.tf

Code Organization:

• Use modules for reusable infrastructure components
• Separate environments with workspaces or directories
• Store state remotely (S3 + DynamoDB for AWS, GCS for GCP, Azure Blob for Azure)
• Use variables for environment-specific values
• Never commit secrets (use AWS Secrets Manager, HashiCorp Vault, etc.)

Terraform Workflow:

# Initialize
terraform init

# Plan (review changes)
terraform plan -out=tfplan

# Apply (execute changes)
terraform apply tfplan

# Destroy (when needed)
terraform destroy

Best Practices:

• Use terraform fmt for consistent formatting
• Use terraform validate to check syntax
• Implement state locking to prevent concurrent modifications
• Use terraform import for existing resources
• Version pin providers: required_version = "~> 1.5"
• Use data sources for referencing existing resources
• Implement depends_on for explicit resource dependencies

Kubernetes Deployment Patterns

Deployment Strategies:

• Rolling Update: Gradual replacement of pods (default)
• Blue/Green: Run two identical environments, switch traffic
• Canary: Gradual traffic shift to new version
• Recreate: Terminate old pods before creating new ones (downtime)

Resource Management:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
        - name: myapp
          image: myapp:v1.0.0
          resources:
            requests:
              memory: '256Mi'
              cpu: '250m'
            limits:
              memory: '512Mi'
              cpu: '500m'
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080

Best Practices:

• Use namespaces for environment/team isolation
• Implement RBAC for access control
• Define resource requests and limits
• Use liveness and readiness probes
• Use ConfigMaps and Secrets for configuration
• Implement Pod Security Policies (PSP) or Pod Security Standards (PSS)
• Use Horizontal Pod Autoscaler (HPA) for auto-scaling

CI/CD Pipeline Patterns

GitHub Actions Example:

name: CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run tests
        run: npm test

  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      - name: Push to registry
        run: docker push myapp:${{ github.sha }}

  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - name: Deploy to Kubernetes
        run: kubectl set image deployment/myapp myapp=myapp:${{ github.sha }}

Best Practices:

• Implement automated testing (unit, integration, e2e)
• Use matrix builds for multi-platform testing
• Cache dependencies to speed up builds
• Use secrets management for sensitive data
• Implement deployment gates and approvals for production
• Use semantic versioning for releases
• Implement rollback strategies

Infrastructure as Code (IaC) Principles

Version Control:

• Store all infrastructure code in Git
• Use pull requests for code review
• Implement branch protection rules
• Tag releases for production deployments

Testing:

• Use terraform plan to preview changes
• Implement policy-as-code with Sentinel, OPA, or Checkov
• Use tflint for Terraform linting
• Test modules in isolation

Documentation:

• Document module inputs and outputs
• Maintain README files for each module
• Use terraform-docs to auto-generate documentation

Monitoring and Observability

The Three Pillars:

Metrics (Prometheus + Grafana)

• Use Prometheus for metrics collection
• Define SLIs (Service Level Indicators)
• Set up alerting rules
• Create Grafana dashboards for visualization

Logs (ELK Stack, CloudWatch, Cloud Logging)

• Centralize logs from all services
• Implement structured logging (JSON format)
• Use log aggregation and parsing
• Set up log-based alerts

Traces (Jaeger, Zipkin, X-Ray)

• Implement distributed tracing
• Track request flow across microservices
• Identify performance bottlenecks
• Correlate traces with logs and metrics

Observability Best Practices:

• Define SLOs (Service Level Objectives) and SLAs
• Implement health check endpoints
• Use APM (Application Performance Monitoring) tools
• Set up on-call rotations and runbooks
• Practice incident response procedures

Container Orchestration (Kubernetes)

Helm Charts:

• Use Helm for package management
• Create reusable chart templates
• Use values files for environment-specific configuration
• Version and publish charts to chart repository

Kubernetes Operators:

• Automate operational tasks
• Manage complex stateful applications
• Examples: Prometheus Operator, Postgres Operator

Service Mesh (Istio, Linkerd):

• Implement traffic management (canary, blue/green)
• Enable mutual TLS for service-to-service communication
• Implement circuit breakers and retries
• Observe traffic with distributed tracing

Cost Optimization

AWS Cost Optimization:

• Use Reserved Instances or Savings Plans for predictable workloads
• Implement auto-scaling to match demand
• Use S3 lifecycle policies to transition to cheaper storage classes
• Enable Cost Explorer and set up budgets
• Right-size instances based on usage metrics

Multi-Cloud Cost Management:

• Use tags/labels for cost allocation
• Implement chargeback models for team accountability
• Use spot/preemptible instances for non-critical workloads
• Monitor unused resources (idle VMs, unattached volumes)

Cloudflare Developer Platform

Cloudflare Workers & Pages:

• Edge computing platform for serverless functions
• Deploy at the edge (close to users globally)
• Use Workers KV for edge key-value storage
• Use Durable Objects for stateful applications

Cloudflare Primitives:

• R2: S3-compatible object storage (no egress fees)
• D1: SQLite-based serverless database
• KV: Key-value storage (globally distributed)
• AI: Run AI inference at the edge
• Queues: Message queuing service
• Vectorize: Vector database for embeddings

Configuration (wrangler.toml):

name = "my-worker"
main = "src/index.ts"
compatibility_date = "2024-01-01"

[[kv_namespaces]]
binding = "MY_KV"
id = "xxx"

[[r2_buckets]]
binding = "MY_BUCKET"
bucket_name = "my-bucket"

[[d1_databases]]
binding = "DB"
database_name = "my-db"
database_id = "xxx"

Consolidated Skills

This expert skill consolidates 1 individual skills:

• cloudflare-developer-tools-rule

Related Skills

• docker-compose - Container orchestration and multi-container application management

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing: Record any new patterns or exceptions discovered.

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.