Boost Cloud API Scripts with Async Python and Azure SDK v4

🚀 How do you create and deploy a simple AWS Lambda function using Python? Day 35 / 100 of #100DaysOfCloud ✅ Today I worked on building a serverless function using AWS Lambda, focusing on execution roles and response handling. 🔹 Task Overview The goal was to create a Lambda function that returns a custom message with a proper status code using Python runtime. 🔹 Steps Performed ✅ Created a Lambda function named devops-lambda ✅ Selected Python runtime ✅ Created and attached an IAM role lambda_execution_role ✅ Wrote function code to return response: Body → "Welcome to KKE AWS Labs!" Status Code → 200 ✅ Deployed the function using AWS Console ✅ Tested the function to verify correct output 🔹 Result Successfully deployed a serverless Lambda function that returns the expected response with status code 200, confirming proper configuration and execution. 💡 Why this matters AWS Lambda enables event-driven, serverless computing, reducing infrastructure management while allowing scalable and efficient application execution. Continuing to strengthen my hands-on experience with AWS serverless services, IAM roles, and cloud automation. #AWS #DevOps #Lambda #Serverless #CloudComputing #Python #100DaysOfCloud

🚀 Built a Scalable Distributed Web Scraping System with Kubernetes I recently designed and implemented a distributed scraping pipeline using Kubernetes to handle large-scale data extraction efficiently. 🔧 What I implemented: - Distributed task queue using Redis - Multiple scraper workers running as Kubernetes pods - Auto-scaling based on workload - Fault-tolerant and self-healing architecture - Clean data storage in database & cloud ⚙️ Tech Stack: - Python - Scrapy / Requests - Docker - Kubernetes - Redis 📈 Key Outcomes: - ⚡ 10x faster scraping with parallel workers - 📦 Scalable system handling large workloads - 🔁 Improved reliability with auto-recovery 💡 Key takeaway: Moving from a single scraper to a distributed system significantly improves performance, scalability, and robustness. Always learning. Always building. 💡 #WebScraping #Kubernetes #Docker #Redis #Scrapy #Python #DataEngineering #DistributedSystems #DevOps #BigData #DistributedpScraping #Automation

Latest Project - Fully deployed Sentiment Analysis API built on Azure.   The stack: • Azure Functions (Python 3.11) — serverless compute, no idle cost • Azure AI Language — pretrained sentiment model, no ML training required • Azure API Management — gateway handling routing and policy enforcement • Terraform — entire infrastructure provisioned as code, reproducible and version controlled • Azure Cost Management — budget alerts configured from day one   The API accepts customer review text and returns sentiment (positive/neutral/negative), confidence scores, and extracted key phrases — the kind of data a business can use to track product popularity and customer opinion at scale.   This was a learning project with a real goal: not just getting it to deploy, but being able to explain and defend every architectural decision. I debugged APIM policy tier incompatibilities, route path mismatches, and the distinction between infrastructure provisioning and code deployment — all things that don't show up in tutorials but absolutely show up in production.   Full code and README on GitHub: https://lnkd.in/gvmPDyj7   #Azure #CloudEngineering #Terraform #Serverless #Python #AzureFunctions #DevOps #CloudArchitecture #PortfolioProject

🚀 Excited to share a recent team project: a movie recommendation system built by 3 people. We each worked on a different ML model: SVM Decision Tree KNN The main focus of this project was not only model building, but also API development and cloud deployment. Each model was turned into an API and deployed on Render for testing in a real-world setup. ☁️ A great hands-on experience to strengthen my skills in: Machine learning Flask API development Cloud deployment End-to-end project delivery #MachineLearning #Python #Flask #Render #APIs #CloudComputing #DataScience #TeamProject https://lnkd.in/eSF2JU37

For my bachelor project, AI Code Review, I did not use any cloud API's. Everything ran locally, on a self hosted server, the company owned. Why? - Data sensitivity (company code) - Full control over infrastructure - No external dependencies Setup: - Ollama (local LLM inference) - Python backend (FastAPI) - Async requests + benchmarking - SQLite + validation layer Tradeoffs: - Lower raw model performance - Higher control, security, and predictability In many real-world cases, 👉 control > raw model intelligence #SelfHostedAI #Security #Backend #LLM

👉 Designing Backend APIs for Kubernetes-Based Systems When building backend APIs, it’s easy to focus only on functionality — endpoints, validation, database logic. But once that API runs inside Kubernetes on AWS, the design requirements change. An API is no longer just code. It becomes part of a distributed system. Here are a few lessons I’ve learned while deploying Python backend services in Kubernetes environments: 1️⃣ Design for Statelessness Kubernetes pods are ephemeral. They restart, reschedule, and scale dynamically. If your API depends on in-memory state, scaling becomes unpredictable. Externalizing session data (Redis, databases, object storage) makes scaling clean and reliable. 2️⃣ Health Checks Are Critical Liveness and readiness probes are not optional. Liveness → determines when a container should restart Readiness → controls traffic routing Poorly designed health checks can cause cascading restarts or traffic misrouting. 3️⃣ Resource Awareness Matters Backend APIs must: Handle CPU throttling gracefully Avoid memory leaks Respect defined resource limits Otherwise, scaling won’t solve performance problems. 4️⃣ Observability from Day One Logging, metrics, and tracing should be embedded into the service. Without visibility, debugging in distributed environments becomes guesswork. The biggest shift for me: Building APIs for Kubernetes means thinking beyond code — it means designing for scale, failure, and automation. When backend logic, cloud infrastructure, and orchestration work together intentionally, systems become predictable and resilient. Next week, I’ll share thoughts on cost optimization strategies in Kubernetes environments. #Kubernetes #BackendEngineering #Python #AWS #CloudNative #DevOps #APIDesign #PlatformEngineering

✅ Day 141/365 — Streams, Sorted Lists & Enterprise Features No days off. Here's what went down today: Cloud — AWS Kinesis Data Streams Dived deep into Kinesis Data Streams — one of the core building blocks for real-time data pipelines on AWS. Went beyond just the concept; spun up an actual stream, produced data into it, and consumed it on the other end. Seeing the data flow in real time hits different. LeetCode — Merge Two Sorted Lists Solved it. 0ms runtime — beating 100% of all Python submissions. The approach: collect both lists, sort, rebuild the linked list. Clean and effective. Building — Product is getting serious Today's shipped features: → Role-based access (owner / editor / viewer) → Invite system with real, working links → Code version history (think Git, but lite) → Product is starting to feel genuinely enterprise-level Every day, the gap between where I started and where I'm going gets clearer. 141 days in — no looking back. #365DaysOfCode #AWS #Kinesis #CloudComputing #LeetCode #Python #BuildInPublic #SoftwareEngineering #Day141

Anyone can build an app that works when things go right. I wanted to build a system that survives when things go wrong. Most portfolio projects often end with simple interactions like "user clicks a button, a database updates." I aimed to create something that truly breaks, recovers, and scales. Over the past few weeks, I developed a fully serverless AWS event-driven system that simulates an end-to-end factory production line. https://lnkd.in/dGHN7Tud Instead of a monolithic backend, I designed an event-driven flow where state changes dictate the next action, eliminating manual orchestration and relying solely on events. The Architecture & The "Why": - API Gateway + Cognito (JWT): Securing and throttling the edge. - DynamoDB + Streams: The source of truth, where a payment update automatically triggers the next phase via Streams. - SQS + DLQ: The shock absorbers, decoupling the storefront from the factory floor to prevent traffic spikes from crashing the processing engine. - EventBridge (Scheduler): The watchdog, monitoring for edge cases, such as orders stuck in production for over 24 hours. - SNS: Real-time alerting for inventory drops and factory delays. - Lambda (Python): The stateless glue that holds the business logic together. This project forced me to confront the realities of distributed systems: handling failures gracefully, avoiding tight coupling, and keeping cloud costs near $0 for idle workloads. My next optimization will be implementing ElastiCache to enhance read-heavy paths. I am focusing my work on architectures that not only function but also survive failure. For those building in the serverless space: How do you prefer to manage complex, multi-step workflows without creating a tangled web of dependencies? Step Functions, or pure event choreography? #AWS #Serverless #EventDriven #SoftwareArchitecture #CloudComputing #EventDrivenArchitecture #DistributedSystems #Microservices #SystemDesign #BackendEngineering #AmazonWebServices #CloudNative #AWSLambda #DynamoDB #CloudArchitecture #Python #PythonDeveloper #BackendDeveloper #Coding #SoftwareEngineering #Scalability #Resilience #FinOps

Building Model Context Protocol (MCP) servers from scratch is a waste of your time. Connecting LLMs to your private data shouldn't take weeks of engineering. Yet most developers get completely bogged down in boilerplate code before they even think about deployment. There is a much faster way to ship. In today's daily audio pill, we break down a streamlined workflow to get your Python MCP servers live in record time. Here is the exact stack we cover: • Python for the core logic • Gemini CLI to instantly generate boilerplate • AWS ECS Express for rapid cloud deployment This combination drastically reduces the friction of giving your AI models secure context. Stop fighting with infrastructure and start leveraging your data. Listen to the short episode or read the full script to steal this deployment strategy. You can find the link to the full newsletter in the comments below. 🎙️ #AI #Python #CloudComputing

Late nights, deep dives, and real fundamentals. Revisiting the core of databases — Indexing, ACID properties, and Transaction management — because scalable systems are built on strong basics, not shortcuts. In a world chasing frameworks, I’m focusing on what actually drives performance under the hood. Consistency. Isolation. Optimization. That’s where real engineering begins. #KeepBuilding #DeepWork #softwareengineering #backenddevelopment #fullstackdeveloper #database #databases #sql #postgresql #mysql #nosql #mongodb #dbms #systemdesign #scalability #performance #optimization #indexing #transactions #acidproperties #backendengineer #coding #programming #developerlife #tech #technology #engineering #computerscience #webdevelopment #api #nodejs #javascript #java #python #devlife #codinglife #buildinpublic #learning #learningeveryday #continuouslearning #growthmindset #careergrowth #techcareer #developers #100daysofcode #code #coders #programmers #softwaredeveloper #devcommunity #techtalk #engineeringlife #backend #frontend #fullstack #cleancode #bestpractices #architecture #systemarchitecture #distributedsystems #microservices #cloud #aws #azure #gcp #devops #datastructures #algorithms #interviewprep #techinterview #sqltips #databasemanagement #queryoptimization #indexdesign #transactionmanagement #consistency #durability #isolation #atomicity #reliability #performanceengineering #scalable #highperformance #datamodeling #datadesign #backenddev #engineeringmindset #techskills #itindustry #innovation #problemsolving #analyticalthinking #developerjourney #codingjourney #selfimprovement #focus #discipline #deepwork #latenightcoding #workethic #hustle #build #create #learn #improve #shipit #debugging #testing #quality #productionready #reallifeengineering #techlearning #knowledge #notes #studygram #engineeringstudent #softwareengineer #futuretech #itjobs #careergoals #professionalgrowth #linkedindevelopers #networking #community #shareknowledge #mentor #mentorship