DevOps & Infrastructure

AWS Cloud Services

Amazon Web Services is the backbone of my cloud infrastructure work. I use AWS when a project needs real compute power, managed databases, file storage at scale, or serverless functions that respond to events. It's not the right fit for every project, but when the requirements call for it, AWS provides the flexibility and reliability I need.

EC2 — Virtual Servers

I provision and manage EC2 instances for applications that need dedicated compute resources. That means selecting the right instance type for the workload, configuring security groups to lock down access, setting up Elastic IPs for stable addressing, and managing SSH keys for secure access. I configure instances with the exact software stack the application requires, whether that's a LAMP setup, a Node.js runtime, or a custom Python environment. I also set up monitoring and alerts through CloudWatch so I know when something needs attention before it becomes a problem.

S3 — Object Storage

S3 handles static asset hosting, file uploads, backups, and media storage across my projects. I configure bucket policies and IAM roles to control access, enable versioning for critical data, and set up lifecycle rules to manage storage costs over time. For static sites or SPAs that don't need a full server, I pair S3 with CloudFront to get fast, cached delivery worldwide. It's also where I store automated database backups, giving me a durable off-server copy of important data.

RDS — Managed Databases

When a project needs a production-grade relational database, I reach for RDS. I typically work with MySQL or PostgreSQL instances, configured with automated backups, Multi-AZ failover for high availability, and properly tuned parameter groups. RDS takes the operational burden of patching and backup management off my plate so I can focus on the application layer. I size instances appropriately for the workload and set up read replicas when query load justifies it.

Lambda — Serverless Functions

Lambda is my go-to for event-driven tasks that don't justify a running server. I use it for processing form submissions, handling webhook payloads, resizing uploaded images, running scheduled data jobs, and connecting services together through API Gateway. The cost model is compelling for intermittent workloads since you only pay for actual execution time. I write Lambda functions in Node.js or Python, package dependencies with deployment zips or container images, and manage configuration through environment variables and IAM policies.

Docker Containerization

Docker changed the way I build and ship software. Every non-trivial application I work on gets a Dockerfile and a docker-compose configuration. Containers give me a consistent environment from my local machine all the way through to production, eliminating the classic "it works on my machine" problem.

I write multi-stage Dockerfiles to keep production images lean, separating build dependencies from runtime requirements. A typical setup might include a Node.js build stage that compiles assets, followed by a minimal runtime image that serves the application. For local development, I use docker-compose to spin up the full stack: application server, database, cache layer, and any supporting services, all connected on an isolated network.

On the deployment side, I push images to container registries (Amazon ECR or Docker Hub) and pull them onto production servers. This workflow means I can roll back to any previous image tag if a deployment introduces issues. It also makes horizontal scaling straightforward since every container instance is identical and stateless by design.

Docker also plays well with the CI/CD pipelines I set up. Automated builds create fresh images on every push to the main branch, run tests inside the container environment, and push validated images to the registry ready for deployment.

Linux Server Administration

Every cloud server I manage runs Linux, typically Ubuntu LTS or Amazon Linux 2. I handle the full server lifecycle: provisioning, hardening, software installation, monitoring, and ongoing maintenance.

Security is the first priority on any new server. I disable root SSH login, enforce key-based authentication, configure UFW or iptables firewall rules to allow only necessary traffic, set up fail2ban to block brute-force attempts, and keep packages updated with unattended-upgrades for critical security patches. I also configure SSL/TLS certificates through Let's Encrypt with automated renewal so every site and API endpoint uses HTTPS.

For web servers, I work with both Nginx and Apache depending on the project requirements. Nginx is my default choice for reverse proxying Node.js or Python applications, serving static files, and handling SSL termination. I tune worker processes, buffer sizes, and caching headers based on the expected traffic patterns. For PHP applications, I configure Apache with mod_php or PHP-FPM behind Nginx, optimizing opcache settings and connection pools for the workload.

I set up log rotation, disk usage monitoring, and process supervision with systemd to keep services running reliably. When something goes wrong at 2 AM, I want the system to recover automatically whenever possible and alert me about anything that needs manual intervention.

Git Version Control

Git is non-negotiable on every project. I use it for source control, collaboration history, and as the trigger point for automated deployments. Every repository follows a consistent branching strategy: main holds production-ready code, development branches isolate work in progress, and feature branches keep individual changes contained until they're reviewed and ready.

I write clear, descriptive commit messages that explain the intent behind changes, not just what changed. This makes the project history useful months or years later when I need to understand why a particular decision was made. I use tags to mark release versions, making it easy to identify and roll back to any specific release.

For projects with multiple contributors, I enforce pull request workflows with required reviews before merging to main. Branch protection rules prevent direct pushes to production branches, and status checks from CI pipelines must pass before a merge is allowed. This keeps the main branch stable and deployable at all times.

I host repositories on GitHub and use its built-in features extensively: issue tracking for task management, project boards for sprint planning, and GitHub Actions for automation. The tight integration between code, issues, and deployments keeps everything in one place.

CI/CD Pipelines

Continuous integration and continuous deployment pipelines remove the friction and risk from getting code into production. I build CI/CD workflows primarily with GitHub Actions, though I've also worked with other platforms when the project calls for it.

A typical pipeline I set up includes several stages. First, the build stage installs dependencies, compiles assets, and produces the deployment artifact. Next, automated tests run against the build to catch regressions before they reach production. If tests pass, the pipeline deploys to a staging environment for a final check. After staging validation, a production deployment runs automatically or waits for manual approval, depending on the project's risk tolerance.

I configure pipelines to handle environment-specific variables securely using GitHub Secrets, run database migrations as part of the deployment process, invalidate CDN caches after static asset changes, and send notifications to Slack or email when deployments succeed or fail. For containerized applications, the pipeline builds Docker images, pushes them to the registry, and triggers a rolling update on the target servers.

The goal is always the same: make deployments boring. When shipping code is a routine, automated process, I can focus on building features instead of worrying about whether the deployment will break something. Smaller, more frequent deployments also mean each release carries less risk and is easier to debug if something does go wrong.

Vercel for Frontend Deployments

Vercel is my preferred platform for deploying frontend applications, static sites, and Next.js projects. It provides an incredibly streamlined deployment experience: connect a Git repository, configure build settings, and every push to main triggers an automatic production deployment. Preview deployments spin up automatically for every pull request, giving me a live URL to test changes before merging.

The edge network handles global CDN distribution, automatic HTTPS, and smart caching without any configuration on my part. For Next.js applications specifically, Vercel offers optimized support for server-side rendering, API routes, incremental static regeneration, and image optimization right out of the box.

I use Vercel for projects where the frontend is the primary deliverable and the infrastructure requirements are straightforward. It handles custom domains, environment variables, serverless functions, and analytics. The platform removes the need to manage web servers, SSL certificates, or CDN configurations for these types of projects, which lets me ship faster and spend less time on operations.

For client projects that need a simple, reliable hosting solution without ongoing server management overhead, Vercel is often the most cost-effective and maintainable choice. The free tier covers many small projects, and the Pro tier provides everything a production application needs.

Choosing the Right Platform

Not every project needs the same infrastructure. I match the hosting platform to the project's actual requirements rather than defaulting to the most complex option. Here's how the three main approaches compare:

I often use a combination of these platforms on a single project. A common pattern is a Next.js frontend deployed to Vercel with an API backend running on AWS, connected through environment variables and CORS configuration. This gives each layer the platform best suited to its needs while keeping the overall architecture simple and maintainable.

How I Approach Infrastructure

As a solo developer, I have to be pragmatic about infrastructure choices. I don't over-engineer systems for traffic they'll never see, and I don't cut corners on security regardless of project size. Every decision comes down to reliability, maintainability, and cost efficiency.

I document every infrastructure decision and configuration. If I get hit by a bus tomorrow, someone else should be able to look at the repository, read the documentation, and understand exactly how everything is set up and why. That means infrastructure-as-code where possible, clear README files, and environment variable documentation for every project.

Monitoring and alerting are part of every production deployment. I set up health checks, uptime monitoring, error tracking, and performance metrics so issues surface quickly. Automated backups with tested restore procedures protect against data loss. And I keep dependencies updated to patch security vulnerabilities before they become a problem.

The tools change over time, but the principles stay the same: automate repetitive tasks, version control everything, keep environments consistent, deploy frequently in small increments, and always have a rollback plan. That's the foundation of reliable infrastructure, whether it's a single-page app or a multi-service platform.