Practical field guides on AWS, Kubernetes, Docker, CI/CD, observability, and production infrastructure, alongside the original LinkedIn archive that inspired them.
Long-form DevOps walkthroughs designed for search, reuse, and implementation
Build a production-ready CI/CD pipeline with GitHub Actions and Docker, including secure image promotion, caching, rollout gates, and rollback strategy.
Understand Kubernetes auto scaling with a practical example covering HPA, VPA, Cluster Autoscaler, metrics, and the common tuning mistakes teams make.
Learn how to secure Docker containers in production with hardened images, non-root users, runtime controls, secret handling, and supply-chain checks.
Short-form LinkedIn posts, lessons, and technical snapshots from the original archive
I recently tackled a common but expensive challenge in AWS: the hidden cost of public IPv4 addresses. In a setup with dozens of ECS Fargate tasks, my "In-use Public IP" charges were hitting hundreds of dollars per month. Beyond the cost, having backend workers exposed to the public internet was a security risk I wanted to eliminate. The Fix: I transitioned the entire architecture to a private-first model. 1. Disabled Public IPs: Moved all Fargate tasks to private mode within the VPC. 2. VPC Peering: Connected multiple VPCs using VPC Peering to enable secure, private communication between services across environments, no internet routing required. 3. Optimized Routing: Navigated complex DNS and routing requirements to ensure seamless communication between services without needing a NAT Gateway. 4. Added a Public Load Balancer: Introduced an internet-facing Application Load Balancer to handle inbound traffic. Only the load balancer is publicly accessible backend services remain private. The Results: - Cost: Monthly networking spend for public IPs was eliminated entirely, replaced by a much smaller, fixed endpoint fee. - Security: Drastically reduced the attack surface by ensuring backend workers are no longer reachable from the internet. - Efficiency: The system is now more robust, secure, and cost-predictable.
Most "DevOps problems" aren't tech problems. They're just organizational chaos wearing a YAML hoodie. We love to buy tools to fix culture. It never works. If you want to actually ship faster, try this Rule of Three: 1. Repeat it 3 times? Automate it. 2. Need a 12-step README to run it? You didn't automate it, you just outsourced the confusion. 3. Takes more time to maintain than it saves? Delete it. You've built a monument, not a tool. The Boring DevOps Checklist: Pipelines: Should be idempotent and predictable. If it's "flaky," it's broken. Infra: Treat it like code. If it's not versioned and reviewed, it's a liability. Alerts: If it doesn't require immediate human action? Don't send a page. The Goal: One command to deploy. One dashboard to verify. Stop looking for more tools. Start looking for less surprise.
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$ git commit -m "New portfolio live." I wanted a portfolio that actually reflects my day-to-day work, so I built a CLI-themed portfolio to showcase my DevOps projects. It was built using Replit AI, the fastest way I've found to turn a rough idea into a live website, with a bit of vibe coding along the way.
In today's AI world, almost everyone is using AI to write code. The feature runs. The API responds. The UI looks fine. So we assume the code is good. But here's the uncomfortable truth: 95% of the time, AI doesn't write a real solution — it applies a patch. It fixes the problem for now. Under the hood: - the logic is copied from somewhere else - edge cases are ignored - security is assumed, not verified - technical debt quietly increases Everything works… until it doesn't. This is exactly why tools like SonarQube matter more than ever. Not because AI is bad but because AI is too good at making broken things look correct. SonarQube forces us to slow down for one moment: - check what we're actually shipping - catch issues before production does - stop temporary fixes from becoming permanent systems AI gives speed. SonarQube brings discipline. In an AI-first world, quality doesn't happen by default. It has to be enforced.
Hi, I'm Mojahid Ul Haque, a DevOps Engineer currently pursuing my M.Tech in Cloud Computing from BITS Pilani. This site keeps the original LinkedIn archive intact while extending it with deeper implementation guides on delivery engineering, Kubernetes, observability, AWS architecture, cost control, and production reliability.