Building Multi-Container Todo API with Python Flask and Docker

Gemini CLI is open source and platform agnostic so why not run it on Azure? This dev walks through building a Python MCP server with HTTP transport locally, then deploying it to Azure Kubernetes Service and validating the connection from Gemini CLI over the public endpoint. { author: William McLean + Google Developer Experts } https://lnkd.in/e_Fvp77m

🚀 Running FastAPI on AWS Lambda (with Mangum) Want to build Python APIs without managing servers? You can run FastAPI on AWS Lambda using Mangum, an adapter that bridges ASGI apps with Lambda’s event system. 👉 Why this stack? ⚡ FastAPI = high-performance async APIs ☁️ Lambda = serverless, auto-scaling 🔌 Mangum = connects both seamlessly 💡 Bonus: Use container images in Lambda This lets you package your app + dependencies in Docker and deploy with more flexibility. Flow: Client → API Gateway → Lambda → Container → FastAPI → Response Perfect for modern, scalable, and cost-efficient APIs. #AWS #Lambda #FastAPI #Python #Serverless #CloudComputing #Docker #DevOps #APIs

Just uploaded the full project to GitHub 🔗 If you're learning Azure Functions or serverless architecture, the repo has everything: • Clean commit history that shows the actual development process (not just the final result) • Detailed README breaking down what I built and what I learned • Proper project structure with .gitignore protecting secrets requirements.txt documenting all dependencies function_app.py with the blob trigger implementation • The goal wasn't just to build it, it was to build it the right way. Professional workflow, clean code, honest documentation. Check it out and let me know if you have questions about the project or serverless architecture. GitHub: https://lnkd.in/eziE_HVb #GitHub #AzureFunctions #Python #OpenSource #CloudComputing

I just deployed a simple Python/Flask game to the cloud using a fully automated CI/CD pipeline! 🚀☁️ I wanted to move beyond manual server configurations and build a modern, automated deployment workflow. Here are the technical highlights of the architecture: ✅ Application Development: Refactored a standard Python console script into a stateless web application using the Flask framework. ✅ Infrastructure: Hosted on a Linux-based Azure App Service. ✅ CI/CD: Built a GitHub Actions YAML pipeline to provision a build runner, install dependencies, package the artifact, and push it live on every commit. ✅ Identity & Security: Secured the GitHub-to-Azure connection using a Microsoft Entra ID Service Principal. Applied the principle of least privilege by scoping the RBAC Contributor role strictly to the application's resource group. ✅ Secret Management: Decoupled all sensitive data from the source code. Credentials are locked in GitHub Secrets, and application keys are securely injected via Azure Environment Variables. It’s incredibly satisfying to push code to a main branch and watch the pipeline handle the rest. You can check out the architecture details in my GitHub repo right here! 👇 💻 View the pipeline code: https://lnkd.in/gwt2NSP4 #CloudComputing #Azure #DevOps #CICD #Python #Flask #GitHubActions #CloudArchitecture #InformationSystems #CyberSecurity

🚀 Monitoring Microservices on GCP with Datadog (Spring Boot + Python) As systems scale into microservices, visibility becomes one of the hardest challenges. Debugging a single request across multiple services can quickly turn into guesswork without proper observability. Recently, I explored how to combine Google Cloud Platform (GCP) with Datadog to build a unified monitoring setup for Spring Boot and Python services. Here’s a simplified approach 👇 🔹 Architecture Applications (Spring Boot / Python) → Datadog Agent (running on GCP VM / GKE) → Datadog Platform (Metrics, Logs, Traces) 🔹 Key Capabilities ✔ Distributed tracing across services ✔ Centralized logging ✔ Real-time metrics & dashboards ✔ Service dependency mapping 🔹 Spring Boot Integration Attach Datadog Java Agent Auto-instrument APIs, DB calls, and external requests Add custom traces for critical business flows 🔹 Python Integration Use ddtrace for auto-instrumentation Works seamlessly with Flask / FastAPI Capture request latency and errors with minimal setup 🔹 Why GCP + Datadog? Scales easily with Google Kubernetes Engine Works well with containerized workloads Easy agent deployment via sidecars or DaemonSets 🔹 What I found useful 👉 Correlating logs with traces reduced debugging time significantly 👉 Service maps gave a clear picture of dependencies 👉 Alerts helped catch latency spikes early 🔹 Key takeaway Observability isn’t just about collecting logs—it’s about connecting metrics, traces, and logs to tell a complete story. If you're building microservices on GCP, integrating Datadog early can save a lot of time when things go wrong. Would love to hear how others are handling observability in distributed systems 👇 #GCP #Datadog #Microservices #Observability #SpringBoot #Python #Cloud #DevOps

Just finished building an AWS Resource Health Monitor in Python! This automation bot: - Monitors EC2 instances for idle CPU and missing tags - Stores timestamped health reports in S3 - Auto-remediates issues with least privilege IAM permissions - Schedules itself to run daily with EventBridge Built with Python, boto3, and real AWS infrastructure from scratch. Check it out on GitHub 👇:  https://lnkd.in/eKmp7gWd #AWS #Python #CloudComputing #DevOps #boto3 #CloudPractitioner

Kubernetes Explained For Developers - Save This Cheat Sheet If you're a Python developer getting into Kubernetes, here's everything you need in one carousel: ☸ What Kubernetes actually does (and how it's different from Docker) 📦 Core objects: Pods, Deployments, Services, Ingress 🏨 The Hotel Analogy that makes it all click 💻 Essential kubectl commands (copy-paste ready) 🔒 Config, Secrets, Health Probes ☁️ Cloud K8s: EKS vs GKE vs AKS ✅ Production checklist for your first deploy The fastest way to learn: build one FastAPI app → Dockerfile → Deployment → Service → scale to 3 replicas. That one exercise teaches more than hours of theory. Save 🔖 and share ↗️ with a developer who needs this. #Kubernetes #DevOps #Python #CloudNative #Docker #SoftwareEngineering #K8s #FastAPI #Backend #TechCareer

This case study shows building a centralized multi-account AWS monitoring platform managing 25+ accounts using Python Boto3 to fetch resource configurations into MongoDB, with Flask API and Next.js frontend achieving $30k annual savings. More: https://ku.bz/LV7qH0CK1

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

How many unused Lambda functions are running in your AWS account right now? I built a simple Python script that helped identify 53 wasteful functions within minutes,highlighting gaps in visibility and cost optimization in serverless setups. Take a look: https://lnkd.in/d9evji-B