DevOps Reference Architecture: From Code to Scalable Deployment
3 min read

I recently started my core DevOps learning journey — and understanding the DevOps Reference Architecture is my very first step into it. This architecture forms the foundation of how real-world systems are planned, built, deployed, and scaled efficiently.
In this blog, I’ll walk you through a real-world DevOps Reference Architecture, from planning and coding to deploying a blog application in production using GitHub, Jenkins, Docker, Kubernetes, Terraform, Prometheus, and more.
What Is DevOps Reference Architecture?
A DevOps Reference Architecture is a complete blueprint for how code moves from a developer’s machine to a production server — with automation, scalability, and monitoring built-in.
It typically includes:
Planning & collaboration tools
Continuous Integration / Continuous Deployment (CI/CD)
Infrastructure as Code (IaC)
Containerization & Orchestration
Monitoring & Feedback
Full DevOps Flow by example ( Blog App )
We’ll use a simple use case: a blog app where users can register, write posts, and view content.
Each phase includes tools and responsibilities:
1️⃣ PLAN
Goal: Define what to build and how to deliver.
Activities:
Product team creates user stories
Designers share mockups
Sprint planning in Jira
Tools: Jira, Figma, Confluence
2️⃣ DEVELOP
Goal: Build the frontend and backend code.
Activities:
React for frontend, Node.js for backend
Code pushed to GitHub
Feature branches & Pull Requests
Tools: Git, GitHub, VS Code, Postman
3️⃣ BUILD
Goal: Build and containerize the app
Activities:
CI pipeline triggered on push
Run
npm build& create Docker image
Tools: Jenkins, GitHub Actions, Docker
4️⃣ TEST
Goal: Verify functionality, catch bugs early
Activities:
Run unit tests, integration tests
Code linting and analysis
Tools: Jest, Cypress, ESLint, SonarQube
5️⃣ RELEASE
Goal: Package the app and prepare for deployment
Activities:
Push Docker image to registry
Tag release
v1.0.0
Tools: DockerHub, AWS ECR, Git Tags
6️⃣ DEPLOY
Goal: Launch the app on infrastructure
Activities:
Terraform provisions EC2, networking
Ansible installs Docker, Node, NGINX
Kubernetes (on EC2 or EKS) runs pods
Ingress routes traffic
Tools: Terraform, Ansible, Kubernetes, NGINX Ingress
7️⃣ AUTO SCALING
Goal: Handle more traffic with scale
Activities:
HPA adds pods when CPU/memory spikes
Cluster Autoscaler adds EC2 nodes
Tools: HPA, Cluster Autoscaler, AWS EC2, EKS
8️⃣ OPERATE
Goal: Monitor app health & log activity
Activities:
Track metrics and logs
Setup alerts for failures or high load
Tools: Prometheus, Grafana, ELK, Alertmanager
9️⃣ FEEDBACK & IMPROVE
Goal: Use real feedback to improve product
Activities:
QA and user feedback → Jira
Start next sprint with improvements
Tools: Jira, GitHub Issues, Slack
Here are some architecture examples
AWS Architecture

AZURE Architecture

Real-World Benefits of This Architecture
✅ Scalable deployments ✅ Quick rollback and testing ✅ Built-in monitoring and auto-healing ✅ Faster feature delivery
DevOps Reference Architecture Summary
PLAN → DEVELOP → BUILD → TEST → RELEASE → DEPLOY → AUTO SCALE → OPERATE → FEEDBACK
Each stage connects with the next using automation and real-time feedback.
Here is the tools per stage
| Stage | Tools Used |
| Plan | Jira, Figma |
| Develop | Git, GitHub, VSCode |
| Build | Jenkins, Docker |
| Test | Jest, Cypress, SonarQube |
| Release | DockerHub, AWS ECR |
| Deploy | Terraform, Ansible, K8s, NGINX |
| Scale | HPA, Cluster Autoscaler, EC2 |
| Operate | Prometheus, Grafana, ELK, Alertmanager |
| Feedback | Jira, GitHub Issues |
🔗 Stay Tuned
I’ll be sharing more breakdowns of each step with YAML examples, CI/CD pipeline files, and real deployments.
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