Kubernetes Cheat Sheet for Python Developers

Just published a deep-dive blog on Docker + Python 🐳 Covered everything from scratch: → What Docker actually is (with a Maggie noodles analogy, yes) → Writing your first Dockerfile → Docker Compose for multi-service apps → Production setup with Gunicorn + Nginx → Deploying on AWS EC2 If you've been confused about containers, images, port mapping, or why your app "works on your machine" but breaks on the server - this one's for you. Link https://lnkd.in/gcn7atem #Docker #Python #DevOps #WebDevelopment #Flask #CloudComputing #AWS #Programming #100DaysOfCode #TechBlog

🐳 Optimized Docker images = Faster deployments! Reduced my Python app image from 1.6GB → 96.9MB — that's a 94% size reduction! Using gcr.io/distroless + multi-stage builds to ship lean, production-ready containers. 🚀 #Docker #DevOps #AWS #Python #Optimization #CloudEngineering

Why am I still writing Bash when Python can do this better? So I started documenting the switch. Just published 3 new posts on my blog: 🐍 Phase 0: Python Basics for People Who Already Know Bash 🐍 Phase 1: Replace Your Shell Scripts with Python 🛠️ Project: Automated CI/CD Pipeline for a Flask App (Docker + Nginx + GitHub Actions + AWS EC2) Everything I'm learning, in public. If you're a DevOps engineer curious about Python or MLOps, I'm writing this roadmap for you. 🔗 [https://lnkd.in/gXJmVMTd] #DevOps #Python #MLOps #CI_CD #LearningInPublic #AWS #Docker

Built a small real-time notification system this week using Python, IMAP, and Discord webhooks. The goal was simple — stop manually checking for updates and instead have clean, structured alerts delivered instantly. It pulls in incoming data, processes it, and pushes formatted notifications in real time. Deployed it to the cloud so it runs continuously without any manual effort. Tech stack was straightforward: • Python for processing and logic • IMAP for ingestion • BeautifulSoup for parsing • Discord webhooks for output • Cloud deployment for 24/7 uptime Nothing overly complex, but it’s a good example of how a lightweight system can plug directly into a workflow and improve visibility immediately. Sometimes the most valuable builds aren’t the biggest ones — they’re the ones that remove friction. Keep Building. #Python #Automation #APIs #Engineering #BuildInPublic

A minimal Python MCP server, validated locally with Gemini CLI, then deployed to AWS ECS Express in a single step. This dev walks through the incremental approach — from stdio transport to HTTP, from local to remote — without the extra noise. { author: William McLean + Google Developer Experts } https://lnkd.in/eQ6R5cbc

A few months ago, I thought Python virtual environments, Docker, and Kubernetes were just different ways to “run code.” Then a small issue changed everything. I had a Kafka consumer working perfectly on my laptop. Clean logic, no errors. But when I moved it to another server… it failed. Missing libraries. Version conflicts. Classic “works on my machine” problem.😀 That’s when I truly understood the role of a Python virtual environment (venv). It helped me isolate dependencies — different projects, different package versions, no conflicts. But the problem wasn’t just Python packages… it was the environment itself. So I moved to Docker. Now, I wasn’t just shipping code — I was shipping the entire environment. Python version, libraries, configurations — everything packed into one image. And suddenly, the same Kafka consumer ran exactly the same everywhere. Problem solved? Not quite. What if the process crashes? What if I need 5 consumers running in parallel? What if one server goes down? That’s where Kubernetes came in. With Kubernetes Pods, my container wasn’t just running — it was being managed. Auto-restarts, scaling, load distribution… things I used to handle manually were now automated. That’s when it clicked: venv helps you develop Docker helps you deploy Kubernetes helps you scale and survive failures Today, I don’t see them as competing tools — they are layers of maturity in building reliable systems. Start simple. But build in a way that you’re ready to scale. #Python #Docker #Kubernetes #Kafka #DevOps #DataEngineering #SystemDesign

What does a multi-agent comic builder look like when deployed across clouds? This dev uses Google's ADK and Gemini LLM to build a low-code Python app, deploy it to AWS Lambda, and run a full agent pipeline that outputs comic book HTML. { author: William McLean + Google Developer Experts } https://lnkd.in/eKaD2nt6 

"What's your tech stack?" Honest answer: Whatever works best for the problem. We use C#, Python, React, Kubernetes, AWS. The usual suspects. But strong engineering goes beyond the tools: > Writing clear, maintainable code > Comprehensive testing (because it matters) > Proper documentation (future-you will thank you) > Meaningful peer reviews that make the work better We choose technologies thoughtfully and build systems designed to scale and last. If you get excited about solving hard problems the right way, you'll fit right in - https://bit.ly/4cjpv9F

A stylized SVG illustration that visualizes the full project in a dark, technical aesthetic. It shows the three-stage pipeline on the left (CSV → Python → LDIF), the AWS VPC with the EC2 server and directory tree on the right, the CloudShell terminal with real command syntax, and key metrics summarized at the bottom. ❤️