DevOps Reference Architecture: From Code to Scalable Deployment

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3 min read

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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

StageTools Used
PlanJira, Figma
DevelopGit, GitHub, VSCode
BuildJenkins, Docker
TestJest, Cypress, SonarQube
ReleaseDockerHub, AWS ECR
DeployTerraform, Ansible, K8s, NGINX
ScaleHPA, Cluster Autoscaler, EC2
OperatePrometheus, Grafana, ELK, Alertmanager
FeedbackJira, 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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