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Cloud-native development
Cloud-native development focuses on building applications that leverage cloud platforms' scalability, resilience, and operational benefits. GitScrum enables teams to track cloud migrations, infrastructure changes, and deployment pipelines in cloud-native architectures.
Cloud-native development fundamentals
Cloud-native applications are designed to exploit cloud computing's full potential through scalable, resilient, and observable architectures. This approach differs from traditional applications by embracing cloud-specific patterns and practices.
Twelve-factor app methodology
Codebase: One codebase tracked in version control, many deployments Dependencies: Explicitly declare and isolate dependencies Config: Store config in environment, not code Backing services: Treat backing services as attached resources Build, release, run: Strictly separate build and run stages Processes: Execute app as one or more stateless processes Port binding: Export services via port binding Concurrency: Scale out via process model Disposability: Maximize robustness with fast startup and graceful shutdown Dev/prod parity: Keep development, staging, and production as similar as possible Logs: Treat logs as event streams Admin processes: Run admin/management tasks as one-off processes
Containerization and orchestration
Docker containerization
Container lifecycle:
Source Code ──► Dockerfile ──► Docker Image ──► Container Registry ──► Deployment
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
Application Build Instructions Portable Package Version Control Runtime Environment
& Dependencies for Container with All Deps & Distribution & Scaling
Dockerfile best practices:
# Use specific base images
FROM node:18-alpine
# Set working directory
WORKDIR /app
# Copy package files first for better caching
COPY package*.json ./
RUN npm ci --only=production
# Copy application code
COPY . .
# Use non-root user
USER node
# Expose port
EXPOSE 3000
# Health check
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD curl -f http://localhost:3000/health || exit 1
# Start command
CMD ["npm", "start"]
Kubernetes orchestration
Pod deployment pattern:
Deployment ──► ReplicaSet ──► Pods ──► Containers
│ │ │ │
│ ▼ ▼ ▼
└─► Service ──► Endpoints ──► Containers ──► Application
Kubernetes resource management:
- Deployments: Declarative application updates
- Services: Network abstraction for pods
- ConfigMaps/Secrets: Configuration management
- PersistentVolumes: Storage abstraction
- Ingress: External access management
Microservices in cloud-native
Service mesh implementation
Istio service mesh architecture:
Application Pods ──► Sidecar Proxy (Envoy) ──► Service Mesh Control Plane
│ │ │
▼ ▼ ▼
Business Logic Traffic Management Configuration
& Data Processing & Observability & Security Policies
Service mesh benefits:
- Transparent service discovery and load balancing
- Built-in observability with distributed tracing
- Security with mutual TLS and authorization
- Traffic management with canary deployments and circuit breakers
API gateway patterns
Kong API gateway:
Client ──► API Gateway ──► Authentication ──► Rate Limiting ──► Routing ──► Services
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼
└─► External Traffic ──► JWT Validation ──► Quota Checks ──► Load Balance ──► Microservices
Gateway responsibilities:
- Request routing and composition
- Authentication and authorization
- Rate limiting and throttling
- Request/response transformation
- Caching and performance optimization
Continuous delivery in cloud
GitOps workflow
Git as source of truth:
Developer ──► Git Commit ──► CI Pipeline ──► Image Build ──► GitOps Repo ──► ArgoCD ──► Cluster
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ ▼
└─► Code Changes ──► Automated Tests ──► Security Scan ──► Manifest Update ──► Sync ──► Deploy
GitOps principles:
- Declarative configuration stored in Git
- Automated deployment from Git state
- Software agents ensure actual state matches desired state
- Pull-based deployment model for security
Infrastructure as code
Terraform cloud deployment:
resource "aws_ecs_cluster" "app_cluster" {
name = "production-cluster"
}
resource "aws_ecs_service" "app_service" {
name = "app-service"
cluster = aws_ecs_cluster.app_cluster.id
task_definition = aws_ecs_task_definition.app.arn
desired_count = 3
load_balancer {
target_group_arn = aws_lb_target_group.app.arn
container_name = "app"
container_port = 3000
}
}
IaC best practices:
- Version control infrastructure code
- Use modules for reusable components
- Implement automated testing for infrastructure
- Plan for state management and locking
- Document infrastructure changes and rationale
Cloud-native security
Zero trust architecture
Identity-aware proxy:
User ──► Identity Provider ──► JWT Token ──► API Gateway ──► Service Mesh ──► Services
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼
└─► Authentication ──► Authorization ──► Request Validation ──► mTLS ──► Business Logic
Security principles:
- Never trust, always verify
- Use least privilege access
- Assume breach and segment networks
- Encrypt data in transit and at rest
- Implement comprehensive monitoring
Secret management
External secret operator:
Kubernetes Secret ──► External Secrets Operator ──► Secret Store (AWS/GCP/Azure)
│ │ │
│ ▼ ▼
└─► Application Pods ──► Mount Secrets ──► Use in Application
Secret management strategies:
- Use managed secret services (AWS Secrets Manager, GCP Secret Manager)
- Rotate secrets regularly with automation
- Implement secret versioning and rollback
- Audit secret access and usage
- Separate secrets by environment and application
Observability and monitoring
Cloud-native observability stack
Three pillars of observability:
Logs ──► Structured Logging ──► Centralized Collection ──► Analysis & Alerting
Metrics ──► Time Series Data ──► Aggregation & Storage ──► Dashboards & Alerts
Traces ──► Distributed Tracing ──► Request Correlation ──► Performance Analysis
Monitoring tools:
- Prometheus: Metrics collection and alerting
- Grafana: Visualization and dashboards
- Jaeger/OpenTelemetry: Distributed tracing
- ELK Stack: Log aggregation and analysis
- Fluentd/Fluent Bit: Log shipping and processing
Alerting and incident response
Alerting hierarchy:
Critical ──► Page on-call engineer immediately
Warning ──► Create ticket for investigation
Info ──► Log for trend analysis
Incident response process:
- Alert triggers based on monitoring thresholds
- Automated diagnosis and initial response
- Human investigation and root cause analysis
- Communication with stakeholders
- Resolution and post-mortem analysis
- Implementation of preventive measures
Serverless computing
Function as a service (FaaS)
AWS Lambda deployment:
Code ──► Package ──► Upload ──► Configure Triggers ──► Deploy Function
│ │ │ │ │
│ ▼ ▼ ▼ ▼
└─► Event Sources ──► API Gateway ──► CloudWatch ──► Auto Scaling
Serverless benefits:
- No server management or provisioning
- Automatic scaling based on demand
- Pay-per-execution pricing model
- Built-in high availability and fault tolerance
- Reduced operational overhead
Serverless challenges
Cold start latency:
- Initial request latency due to function initialization
- Mitigation with provisioned concurrency
- Consider application architecture impact
Vendor lock-in:
- Platform-specific services and APIs
- Migration complexity between providers
- Dependency on cloud provider services
Cloud-native databases
Managed database services
Database as a service options:
Traditional RDBMS ──► Amazon RDS, Google Cloud SQL, Azure Database
NoSQL Databases ──► DynamoDB, Firestore, Cosmos DB
Data Warehouses ──► Redshift, BigQuery, Snowflake
Cache Services ──► ElastiCache, Memorystore, Azure Cache
Database selection criteria:
- Data model requirements (relational, document, graph, etc.)
- Scalability and performance needs
- Consistency and availability requirements
- Cost optimization opportunities
- Integration with application stack
Database migration to cloud
Migration strategies:
- Lift and shift: Direct migration with minimal changes
- Replatform: Minor optimizations for cloud
- Refactor: Significant architecture changes
- Rebuild: Complete rewrite for cloud-native
Migration phases:
- Assessment and planning
- Schema and data migration
- Application code updates
- Testing and validation
- Go-live and monitoring
Cost optimization in cloud
Cloud cost management
Cost allocation and tracking:
Resources ──► Cost Allocation Tags ──► Cost Explorer ──► Budget Alerts ──► Optimization
│ │ │ │ │
│ ▼ ▼ ▼ ▼
└─► Tagging Strategy ──► Usage Analysis ──► Cost Reports ──► Right-sizing
Cost optimization techniques:
- Reserved instances: Commit to longer terms for discounts
- Spot instances: Use spare capacity for non-critical workloads
- Auto scaling: Scale resources based on demand
- Storage tiering: Move data to cheaper storage classes
- Resource cleanup: Remove unused resources automatically
FinOps practices
Financial operations integration:
- Include cost in architecture decisions
- Set budgets and cost controls
- Monitor cost trends and anomalies
- Implement cost-aware development practices
- Regular cost optimization reviews
Cloud-native team culture
DevOps and SRE practices
Site reliability engineering:
Service Level Objectives (SLOs) ──► Error Budgets ──► Reliability Work ──► Incident Response
│ │ │ │
▼ ▼ ▼ ▼
Availability Targets Failure Tolerance Proactive Reliability Blameless Culture
& Performance Goals & Risk Management & Monitoring & Learning
DevOps culture elements:
- Shared responsibility for operations
- Continuous improvement mindset
- Automation of repetitive tasks
- Collaboration between development and operations
- Learning from failures and incidents
Cloud-native team skills
Required competencies:
- Infrastructure as code and automation
- Containerization and orchestration
- Cloud platform services and APIs
- Monitoring and observability tools
- Security best practices for cloud
- Cost optimization and FinOps
Migration to cloud-native
Assessment and planning
Application portfolio analysis:
Applications ──► Complexity Assessment ──► Cloud Readiness ──► Migration Priority ──► Timeline
│ │ │ │ │
│ ▼ ▼ ▼ ▼
└─► Technology Stack ──► Dependencies ──► Business Value ──► Risk Level ──► Resources
Migration readiness factors:
- Application architecture and dependencies
- Data migration complexity and volume
- Security and compliance requirements
- Performance and scalability needs
- Team skills and training requirements
Incremental migration approach
Strangler fig pattern:
Monolith ──► Feature Extraction ──► Microservice Creation ──► API Gateway ──► Traffic Routing
│ │ │ │ │
│ ▼ ▼ ▼ ▼
└─► Identify Bounded Context ──► Create New Service ──► Implement API ──► Gradual Migration
Migration phases:
- Assessment: Evaluate current state and readiness
- Foundation: Set up cloud infrastructure and tooling
- Migration: Move applications incrementally
- Optimization: Tune performance and costs
- Innovation: Leverage cloud-native capabilities
GitScrum integration for cloud-native
Cloud infrastructure tracking
Infrastructure change management:
- Track infrastructure deployments and changes
- Monitor cloud resource utilization
- Manage infrastructure technical debt
- Coordinate multi-service deployments
Cost and resource monitoring:
- Track cloud spending by project and team
- Monitor resource usage and efficiency
- Identify cost optimization opportunities
- Plan capacity and scaling needs
Deployment pipeline management
CI/CD workflow tracking:
- Monitor build and deployment status
- Track deployment frequency and success rates
- Manage release coordination across services
- Implement automated rollback procedures
Quality gate management:
- Track automated testing results
- Monitor security scan outcomes
- Manage approval workflows for deployments
- Ensure compliance with deployment standards
Best practices for cloud-native development
Architecture principles
Design for failure:
- Implement circuit breakers and retries
- Use health checks and graceful degradation
- Design for eventual consistency
- Implement proper error handling and logging
Security by design:
- Implement zero trust principles
- Use encryption everywhere
- Implement least privilege access
- Regular security assessments and updates
Operational excellence
Automation first:
- Automate deployments and infrastructure
- Implement automated testing and monitoring
- Use policy as code for compliance
- Automate incident response where possible
Continuous improvement:
- Regular architecture reviews
- Performance and cost optimization
- Security posture enhancement
- Team skill development and training
Common challenges and solutions
Scalability challenges
Database scaling:
- Implement read replicas for read-heavy workloads
- Use database sharding for write-heavy applications
- Consider NoSQL for specific use cases
- Implement caching strategies
Application scaling:
- Design stateless applications
- Use horizontal pod autoscaling
- Implement queue-based processing
- Use CDN for static content delivery
Reliability challenges
Service dependencies:
- Implement circuit breaker patterns
- Use service mesh for resilience
- Implement proper timeout and retry logic
- Design for graceful degradation
Data consistency:
- Use saga patterns for distributed transactions
- Implement event sourcing where appropriate
- Design for eventual consistency
- Use compensating transactions for failures
Future trends in cloud-native
Emerging technologies
Service mesh evolution:
- Integration with WebAssembly (WASM)
- Enhanced security with SPIFFE/SPIRE
- Multi-cluster and multi-cloud support
- Integration with serverless platforms
Edge computing:
- Distributed application deployment
- Reduced latency for global users
- Integration with IoT and mobile devices
- Privacy-preserving computation
Platform evolution
Kubernetes ecosystem growth:
- Enhanced developer experience with tools like Tilt
- GitOps adoption with tools like Flux and ArgoCD
- Service mesh integration (Istio, Linkerd)
- Enhanced security and policy management
Cloud provider innovation:
- Serverless containers and functions
- AI/ML integration and managed services
- Enhanced security and compliance tools
- Multi-cloud and hybrid cloud solutions