Migrating from monolithic applications to cloud-native architectures unlocks immense agility for delivering customer value faster through continuous innovation.
Amazon Web Services (AWS) provides purpose-built capabilities to empower organizations in embracing cloud-native development while turbocharging velocity through modern DevOps practices.
This guide explores the AWS services ecosystem for cloud-native apps including:
- Adopting Microservices Architecture
- Enabling Rapid Experimentation
- Automating Provisioning
- Shifting Left on Security
- Building Reusable IaC Modules
- Setting up GitOps Pipelines
- Deploying Containerized Workloads
- Monitoring and Observability
- Optimizing Usage and Costs
- Common Anti-Patterns to Avoid
Let’s examine how AWS facilitates the transition towards cloud-native development.
Adopting Microservices Architecture
Decompose monolithic apps into independently scalable microservices by:
● Functional domains aligned to business capabilities
● Loosely coupled interfaces using APIs and events
● Emphasizing decentralized data management
● Cross functional teams with end to end ownership
Enabling Rapid Experimentation
Accelerate exploring ideas through:
AWS Cloud Development Kit: Script local sandbox environments fast using infrastructure as code.
AppRunner: Fully managed service to quickly run container apps without managing servers.
Cloud9: Browser-based IDE eliminating tool setup for building apps on AWS with pre-built templates.
Automating Provisioning
Streamline environment setups through infrastructure as code (IaC) using:
CloudFormation: Orchestrate full AWS stack deployments predictably through templates.
CDK: High level construct library in TypeScript, Python etc to improve developer experience.
Terraform: Codify infrastructure across cloud platforms using declarative definitions to standardize provisioning.
Shifting Left on Security
Build safety checks earlier into development workflow:
Static Analysis: Scan IaC templates, source code and open ports for misconfigurations or risks.
Policy Guardrails: Embed approved boundaries e.g. encrypt data, use managed policies over custom.
Automated Scanning: Frequently scan images and running apps for vulnerabilities.
Building Reusable IaC Modules
Maximize reuse across environments with custom built IaC modules that can be imported across stacks for:
● VPCs and subnets segmentation
● Logging buckets, streams, encryption settings
● Load balancer settings and auto-scaling
● Microservices connectivity and resources
This drives architectural consistency safely.
Setting up GitOps Pipelines
Manage infrastructure through CI/CD principles where:
● Entire system state is declaratively defined in Git
● Changes automatically initiated post merging pull requests
● Promotion across environments follows Git flows
This flattens learning curves through standardized engineering.
Deploying Containerized Workloads
Pick higher level container services vs plain EC2 deployments:
Elastic Container Service (ECS): Orchestrate Docker containers across clusters while handling infrastructure.
Fargate: Serverless version of ECS eliminating capacity planning needs.
Kubernetes (EKS): Popular container orchestrator simplifying running apps across availability zones.
Monitoring and Observability
Understand real world usage and system health by aggregating:
Logs: Centralize application and infrastructure logs with CloudWatch for analysis.
Metrics: Charts for usage patterns, load, errors etc enabling anomaly detection.
Traces: Map end to end request flows across microservices to surface bottlenecks.
Optimizing Usage and Costs
Optimize spending through:
● Auto Scaling groups matching provisioned capacity to workload
● Reserved Capacity discounts for steady usage needs
● Serverless offerings to reduce idle resource waste
● Architecting based on application data patterns
Continuously tune and innovate on usage and efficiency.
Common Anti-Patterns to Avoid
Steer clear of practices causing technical debt like:
● Direct EC2 usage without containerization
● Deep vertical or horizontal scaling of monoliths
● Custom patching, security management at scale
● Specialized skills concentrating on few engineers
● Manual provisioning without Infrastructure as Code
These eventually diminish velocity over time through accrued complexity debt.
Conclusion
Migrating from rigid monoliths to adaptive microservices and leveraging cloud-native development best practices unlocks innovation potential.
AWS purpose-built capabilities augment this transformation through developer empowerment, hardened security, operational excellence and cost efficiency guardrails.
The future will reward those who can continually deliver differentiating capabilities securely through cloud-based execution. By adopting modern architectures and AWS integrated DevOps, engineering teams position themselves competitively.
