In the DevOps industry, knowing YAML syntax is not enough. Employers want to see that you can synthesize automated testing, secure artifact generation, code quality gates, and cloud deployment APIs into cohesive, production-grade architectures. You must prove you can build the factory, not just operate the machines. In this chapter, we outline five robust, professional-grade Continuous Integration and Deployment projects. These projects are designed to validate your mastery of pipeline architecture and form the foundation of an undeniable engineering portfolio.
Job 3 (Testing): Waits for Jobs 1 and 2 to pass. Installs Composer dependencies and executes PHPUnit.
3.The Proof: Create a Pull Request with a deliberate syntax error to show the pipeline blocking the merge. Fix the error, push the commit, and show the pipeline turning "Green."
The Goal: Prove you can bridge the gap between application code and immutable container artifacts.
The Architecture:
1.Source: A Node.js or Python API with a Dockerfile at the root.
2.The Pipeline:
Triggers on push to the main branch.
Logs into Docker Hub securely using GitHub Secrets (DOCKER_USERNAME, DOCKER_PASSWORD).
Executes docker build.
Executes docker push.
Tags the resulting image with the dynamic Git commit hash (${{ github.sha }}).
3.The Proof: Provide screenshots of your Docker Hub repository showing the automatically uploaded, hash-tagged images appearing immediately after a Git commit.
The Goal: Prove you understand Cloud IAM authentication and automated frontend deployment.
The Architecture:
1.Source: A React, Vue, or simple HTML/CSS application.
2.The Pipeline:
Compiles the application (npm install && npm run build).
Uses aws-actions/configure-aws-credentials to authenticate with AWS.
Runs aws s3 sync build/ s3://your-bucket-name --delete to push the compiled artifacts to the live web server.
3.The Proof: Modify the index.html file on your laptop. Run git push. Record a video showing the live website updating automatically 30 seconds later without you touching the AWS console.
The Goal: Prove you can orchestrate advanced, zero-downtime container deployments.
The Architecture:
1.Source: A Kubernetes deployment.yml manifest alongside your application code.
2.The Pipeline:
Builds and pushes a Docker image (Project 2).
Authenticates to a K8s cluster (Minikube, EKS, or GKE) using a vaulted KUBECONFIG secret.
Uses a shell script (sed) to dynamically inject the new Docker Image Tag into the deployment.yml file.
Executes kubectl apply -f deployment.yml.
Verifies the rollout using kubectl rollout status.
3.The Proof: Document the pipeline logs showing the successful sed string replacement and the K8s API response confirming the successful rolling update.
7. Project 5: Infrastructure as Code CI (Terraform)#
The Goal: Prove you understand the "Plan on PR, Apply on Merge" paradigm.
The Architecture:
1.Source: A Terraform repository containing main.tf (e.g., provisioning a basic AWS VPC or EC2 instance).
2.The Pipeline:
On pull_request, the pipeline runs terraform init and terraform plan, generating a simulated output.
On push to main (after the PR is merged), a separate pipeline job runs terraform apply -auto-approve to physically build the cloud hardware.
3.The Proof: Provide screenshots of the GitHub UI showing the automated Terraform Plan output, demonstrating your understanding of safe infrastructure modification.
A CI/CD portfolio is invisible unless you document it perfectly.
The Architecture Diagram: Use a tool like Draw.io or Excalidraw to draw a visual flowchart of your pipeline (e.g., GitHub -> Tests -> Docker Hub -> AWS). Put this at the top of your README.md.
The Badge: Embed the GitHub Actions status badge ([CI]) at the top of your README. A green "passing" badge proves your automation works.
The "Why": Don't just paste YAML. Explain *why* you chose to use Docker layer caching, or *why* you separated the Linting job from the Testing job.
In Chapter 19, we transitioned from learners to automation architects. We designed five capstone projects that synthesize the entire DevOps CI/CD lifecycle. From rigorous Code Quality gates and automated PHPUnit testing, to immutable Docker Image compilation, Kubernetes rolling updates, and Terraform infrastructure provisioning. These projects demand the practical application of YAML syntax, secure secret injection, and cloud API orchestration. By executing these pipelines and meticulously documenting the architectural intent behind them, you construct a professional portfolio that undeniably proves your capability to engineer enterprise automation.
Your pipelines are running, and your portfolio is built. It is time to prepare for the technical screening and map out your career trajectory. Proceed to the final chapter: Chapter 20: Continuous Integration Interview Questions and Career Roadmap.
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