Microsoft Retiring Python Support for Azure App Service and Functions on Windows

🚨 Important Update for Python Developers on Azure! Microsoft has officially announced: ⚠️ Python support on Azure App Service (Windows) & Azure Functions (Windows) will be retired on March 31, 2027 👉 What does this mean? ❌ Your apps will STOP running after this date ❌ No more technical support ✅ App data & configuration will remain 💡 What should you do now? ✔️ Start migrating to Linux-based hosting ✔️ Redeploy your apps on: Azure App Service (Linux) Azure Functions (Linux) 🔥 Key Insight: Python on Windows App Service has already been unsupported since 2021 — this is the final step toward full retirement 💬 My Advice (from real-world experience): Don’t wait till 2027 ⛔ Start migration early → test → optimize → go production ✅ Linux is not just a requirement now… it’s the future for Python workloads on Azure 🚀 #Azure #MicrosoftAzure #AzureAppService #AzureFunctions #Python #DevOps #CloudComputing #AzureUpdates #CloudMigration #Linux #AppService #Serverless #CloudEngineer #AzureDevOps #AzureArchitecture #TechUpdate #CloudNews #ITInfrastructure #Developers #Programming #PythonDeveloper #Kubernetes #AKS #Docker #CloudNative #ModernApps #AzureCloud #TechCommunity #DevCommunity #InfrastructureAsCode #Terraform #CI_CD #CloudSecurity #SRE #Observability #Scalability #CloudTransformation #DigitalTransformation #FutureOfCloud #ITCareers #SoftwareEngineering #BackendDeveloper

DevOps looks fancy from the outside. AWS dashboards Python automation scripts Cool web Uls CI/CD pipelines Everything looks modern and powerful. But when production actually breaks... Suddenly everyone is back to: ssh into the server checking logs in /var/log running random Linux commands writing a quick Bash script to fix something 😅 And that's when you realize something interesting. Behind every fancy cloud platform... behind every automation tool... there is stil: 🐧Linux 💻 Bash 🔨CLI tools Quietly running the entire internet. No fancy UI. No colorful dashboards Just a terminal... and someone who knows what they're doing. DevyOps lesson l learned: You can ignore Bash and Linux at the start. But sooner or later... the terminal will find you.🥲 #DevOps #Linux #Bash #CloudComputing #AWS #Techhumor #CloudEngineer #Automation

A recent conversation started with a simple question — and quickly turned into a hands-on walkthrough: ◽ Launching a server on Amazon EC2 ◽ Connecting via SSH and managing access with key pairs ◽ Configuring security groups (opening the right ports) ◽ Running Python code on a remote Linux machine ◽ Mapping a domain to a server via IP There are countless tutorials online — but sometimes there’s no substitute for sitting down one-on-one and guiding how to do it together. Why not? 👐 Moments like this remind me how powerful it is to break down systems into practical steps — and how quickly things become clear when you actually do them. And honestly? There’s something really satisfying about sharing knowledge that you once learned from someone else... Thanks a lot Yona Swarzman 🙏 I’m continuing to build on this by working on more end-to-end projects involving data, APIs, and deployment workflows. #AWS #CloudComputing #EC2 #Python #KnowledgeSharing

💡 Starting Python on Linux? Here’s a Quick Cheat Sheet If you're beginning your journey with Python in a Linux environment, these basic commands are essential 👇 🔹 Run a Python file python3 file.py 🔹 Check Python version python3 --version 🔹 Make a script executable chmod +x file.py 🔹 Run the script directly ./file.py These may look like small steps, but they form the foundation of working with Python in real-world environments—especially in Cloud and DevOps roles. I’m currently learning and transitioning into Cloud & DevOps, and sharing these small but important learnings along the way. 📌 Save this post if you’re just getting started—it might come in handy. #Python #Linux #DevOps #CloudComputing #PythonForBeginners #LinuxCommands #TechLearning #CareerTransition

Worked on a Cloud File Upload System using Python and Flask. The idea was simple—upload and access files securely through a web interface. Set it up inside a virtual machine and handled basic file storage, upload, and retrieval features. This project gave me hands-on experience with backend development, working in a Linux environment, and understanding how file handling works in real applications. Still learning, but this was a good practical step. #Python #Flask #CloudComputing #BackendDevelopment

Containers can feel very reliable… until they're not. One thing I have seen more times than I can count: An application works perfectly on a developer's laptop, but once it's inside a container, something breaks. Most times, it is not Docker itself that is the problem. Here is what it actually ends up being: 1. Missing Dependencies Your local machine has Node, Python, or a system library installed globally. The container does not. The app runs locally but fails in the container because that dependency was never declared. 2. Environment Variables Your .env file works on your machine, but you forgot to pass it to the container. Suddenly the app cannot find the database connection string or API key. 3. File Paths Windows uses backslashes. Linux uses forward slashes. Your container runs Linux. That hardcoded path C:\projects\data will not work. 4. Assumptions About the Runtime Environment You assumed Python 3.10 is installed. The base image uses 3.8. You assumed /tmp is writable. Maybe it is mounted read-only. Containers force you to be explicit about everything. And that is a good thing. It exposes hidden assumptions and makes your application more portable and reproducible. But only if you pay attention to the details. Here is what I do now: · Always build from a clean base image locally before pushing · Explicitly list every dependency in the Dockerfile · Pass environment variables intentionally, never by accident · Use relative paths or environment-specific path variables · Test the exact same image in staging before production The more predictable your container is, the more reliable your system becomes. #Docker #Containers #DevOps #CloudComputing #AWS #ECS #TheEmpatheticEngineer

🚀 Day 18 – Shell Scripting Level Up! Today I focused on writing cleaner, safer, and reusable shell scripts — a big step from basic scripting to real-world usage 💻 What I learned: ✔️ Writing and calling functions for reusable code ✔️ Using set -euo pipefail for safer scripts ✔️ Handling return values & local variables ✔️ Building a complete system info script One important takeaway: Using set -euo pipefail makes your scripts more reliable and production-ready by preventing silent failures. Key Learnings: Functions = Cleaner & reusable code Strict mode = Safer & error-free scripts Check out my work:https://lnkd.in/g4TvriXU #90DaysOfDevOps #DevOpsKaJosh #TrainWithShubham #DevOps #ShellScripting #Linux #Automation #Scripting #LearningInPublic #TechJourney #Cloud #Programming #CareerGrowth #ITJobs #Developers #CodeNewbie

𝐖𝐡𝐲 𝐃𝐨𝐜𝐤𝐞𝐫 𝐛𝐚𝐬𝐞 𝐢𝐦𝐚𝐠𝐞𝐬 𝐦𝐚𝐭𝐭𝐞𝐫 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 Spent an hour debugging a Dockerfile. The code was fine, the container just wouldn't build. Turned out my slim Python base image was missing the system libraries needed to compile the Postgres driver. 𝐒𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐛𝐚𝐬𝐞 𝐢𝐦𝐚𝐠𝐞 𝐟𝐢𝐱𝐞𝐝 𝐢𝐭 𝐢𝐧 𝐦𝐢𝐧𝐮𝐭𝐞𝐬. Slim images are great until your dependencies have dependencies that go all the way down to the OS. Sometimes it's not even slim vs full, it's which version of slim you're on. Dropping the base from 3.14 to 3.12 was the actual fix. CoderCo #DevOps #Docker #Containers #Cloud #Linux

Built a working SaaS API from zero in 4 hours tonight. No prior setup. No existing codebase. Nothing. FastAPI backend. Cloud database. Live endpoints writing real data. Built on Linux. I work in vendor management by day. Building the tools industries needs by night. The gap between 'I have an idea' and 'I have a working product' is smaller than people think. 4 hours. That's it. #buildinpublic #saas #python #vendortech

Just sitting here casually integrating with low-level Linux kernel APIs. My comfort zone is still Python and TypeScript, talking to hardware through libraries and bindings. Yet here I am working with kernel APIs like 'SG_IO', 'fanotify', and 'clone(CLONE_NEWUSER)' almost as if it required no effort. With my newfound AI powers, I’ve started picking up projects I always wanted to do but kept putting off because of the complexity involved. My hacky solutions worked, but I still wanted to do them properly. 'nonet' is one of them. I wanted something like 'unshare -c -n', but with a working and fully isolated localhost. With plain 'unshare', you can either get loopback working and end up mapped to root, or keep your normal UID and lose any clean path to a working loopback. So I wrote 'nonet' in one evening with help from AI, mostly in Go with a tiny CGO/C helper. Then there is 'spinherd', which took me about two evenings. It takes over power management of spinning disks in my home server. When the disks are idle, it puts them to sleep. On first access, it spins them all back up at the same time. Without it, my RAID6 storage can stall for a solid minute or more because the kernel ends up waking disks one by one. It auto-detects disks, groups them into herds, walks through LVM, dm-crypt, and RAID down to the physical disks, uses 'fanotify' to detect access, and uses 'SG_IO' to send the actual sleep commands. A month ago I would not have believed I’d be casually integrating with kernel APIs without frustration and with this much joy. I knew what I wanted to build. What I did not want was to fight the boilerplate in the middle. Now I do not have to. At this rate I start to fear my years-long backlog of projects might actually be gone by November this year. The 'nonet', 'spinherd', and some of my other projects can be found on my GitHub profile https://lnkd.in/dcg2KEJ4