Deploying AWS Lambda with Python and IAM Role

#100DaysOfCloud - Day 6 of 100 Python asyncio: Make Your Cloud API Scripts 10x Faster Real Problem: A script checked health of 200 Azure resources sequentially. Runtime: 18 minutes. Called nightly. Engineers hated it. After async rewrite: 47 seconds. Same logic. Why Sequential API Calls Kill Your Scripts: # SLOW - sequential, each waits for previous for resource in resources: status = get_status(resource) # network wait results.append(status) # FAST - concurrent I/O import asyncio import aiohttp async def get_status(session, resource): async with session.get(resource['url']) as resp: return await resp.json() async def check_all(resources): async with aiohttp.ClientSession() as session: tasks = [get_status(session, r) for r in resources] return await asyncio.gather(*tasks) results = asyncio.run(check_all(resources)) Key Rules: - asyncio.gather() runs tasks CONCURRENTLY (not threads) - Perfect for I/O-bound work: API calls, DB queries - Use ThreadPoolExecutor for CPU-bound tasks - Use tenacity library for retry logic with async Azure SDK v4+ supports async natively with the aio submodule. from azure.mgmt.compute.aio import ComputeManagementClient Are you using async Python in your cloud automation? #Python #AsyncIO #CloudAutomation #Azure #DevOps #CloudEngineering

AWS Serverless Health Check System (Terraform + Python) I recently built a cloud-based health monitoring system using AWS Lambda, API Gateway, S3, and Terraform to strengthen my hands-on cloud and DevOps skills. What I did: - Developed a Python Lambda function to monitor application health endpoints - Provisioned infrastructure using Terraform (IAM, API Gateway, S3) - Designed modular, reusable infrastructure as code - Implemented secure, least-privilege IAM policies - Solved dependency challenges using Terraform references instead of hardcoding Check out the full project and code here: https://lnkd.in/eYxgUzy6 #AWS #CloudComputing #DevOps #Terraform #InfrastructureAsCode #Serverless #AWSLambda #APIGateway #S3 #Python #CloudEngineering #SoftwareEngineering #TechProjects #GitHub #OpenToWork

🚀 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

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐚𝐭𝐢𝐜 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 𝐒3 + 𝐁𝐨𝐭𝐨3 Integrating the 𝐀𝐖𝐒 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐃𝐊 with my 𝐂𝐋𝐈 to automate S3 deployments and using a python script to create resources on AWS cloud. As part of my AWS Cloud Practice, I have setup my environment configuration through the CLI. Today I automated S3 bucket creation using Python. My 𝐆𝐢𝐭𝐡𝐮𝐛 𝐥𝐢𝐧𝐤 to AWS Cli + Python SDK repo: https://lnkd.in/d53mp-JN 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: 1. Configure AWS CLI with IAM credentials. 2. Use Boto3 to initialize an S3 client. 3. Handle regional endpoints and constraints programmatically. "Building the cloud" is exciting, can't wait to integrate with Agentic Ai Applications. It was interesting to find out that the first service AWS ever launched was s3 and it was at the us-east-1 region, which is the only reason why you have to use LocationConstraints when you want to create resources in other regions to create endpoints. #AWSArchitecture #PythonDeveloper #InfrastructureAsCode #AWSCLI #Tech #AWS #Python

I set up an AWS Lambda function using Python and Boto3 to run my inventory pipeline. Now, whenever a batch photo lands in S3, the Lambda quickly adds product data to my DynamoDB table. After my last post, I got helpful feedback about "permission gaps." It’s tempting to skip over IAM details when you just want your code to run. But as a programmer, I know that getting it to work is only the beginning. Next, I’ll be integrating AWS Rekognition so the system can automatically identify the product. #AWS #Terraform #DevOps #BuildingInPublic #CloudEngineering #Python #SoftwareEngineering

Cold starts in serverless systems are often underestimated until you hit real workloads. While building FastAPI services on AWS Lambda, I ran into latency spikes that didn’t show up in testing but became obvious under burst traffic. A few observations: • Packaging size has a bigger impact than expected • VPC configuration can significantly increase cold start time • “warm” strategies help, but don’t fully eliminate the issue What worked better: - keeping functions minimal and focused - avoiding unnecessary dependencies - moving latency-sensitive paths off Lambda when needed Serverless is powerful, but it’s not “free abstraction” - you still need to understand what’s happening under the hood. Would be interesting to hear how others are handling cold start trade-offs in production. #aws #serverless #backend #python

🚀 Python on AWS – Scalable Backend Systems Built and deployed backend systems using Python (FastAPI/Django) on AWS, focusing on scalable and high-performance architectures. ☁️ AWS (EC2, Lambda, ECS, EKS, S3, RDS, DynamoDB) ⚙️ REST APIs & Microservices 🔄 Docker, CI/CD (Jenkins, GitHub Actions) 📊 Redis caching & performance optimization 🔐 IAM, security best practices & encryption 🗄️ Database design (PostgreSQL, NoSQL) Always exploring better ways to build cloud-native, distributed systems. #Python #AWS #CloudComputing #Microservices #BackendDevelopment #DevOps #SystemDesign

🚀 Serverless in Action: AWS Lambda + S3 Event Trigger As part of my DevOps learning, I built a simple AWS Lambda function that gets triggered whenever a file is uploaded to an S3 bucket. 👉 The function extracts key details like: Bucket name File name File size Here’s a simplified version of the script 👇 import json def lambda_handler(event, context): kp = event['Records'][0]['s3'] bucket = kp['bucket']['name'] file_name = kp['object']['key'] file_size = kp['object']['size'] print(f"Bucket: {bucket}") print(f"File: {file_name}") print(f"Size: {file_size} bytes") 💡 What I learned: ✅ How S3 event notifications trigger Lambda functions ✅ How to parse JSON event structures in Python ✅ Real-time processing of uploaded files ✅ Basics of serverless architecture 🔧 Next steps: Add error handling & logging (CloudWatch) Decode S3 object keys properly Integrate with SNS/Slack for alerts Extend to automate workflows (e.g., file validation, ETL) Serverless is powerful — no servers to manage, just focus on logic 🚀 How are you using AWS Lambda in your projects? #AWS #Lambda #Serverless #DevOps #CloudComputing #Python #S3

❓ Why Security Must Be Built Into Cloud-Native Systems from Day One As systems move to AWS and Kubernetes, security becomes more complex — not less. When I first started working in cloud environments, I thought security was mostly about IAM roles and network policies. But in real-world backend and data platforms, security touches everything: How services authenticate with each other How secrets are stored and rotated How containers are configured and scanned How logs and telemetry are protected How least-privilege access is enforced In Kubernetes environments especially, small misconfigurations can have large impacts. For example: Overly broad IAM permissions Hardcoded secrets in environment variables Open security groups Missing role-based access control (RBAC) The shift for me was realizing this: Security is not a final review step. It’s part of application design. When building Python services running on Kubernetes in AWS, I now think about: IAM roles instead of static credentials Kubernetes secrets management strategies Network policies for service isolation Observability tools to detect abnormal behavior Infrastructure-as-code to avoid manual configuration drift The goal isn’t just to pass audits. It’s to build systems that are secure by default. Cloud-native engineering gives us powerful tools — but it also requires discipline. I’ll share insights on designing scalable backend APIs for Kubernetes environments. #CloudSecurity #Kubernetes #AWS #BackendEngineering #CloudNative #DevOps #Python #InfrastructureAsCode #PlatformEngineering

🚀 Day 7/25 — Why your Docker image is probably too big Most beginners write a Dockerfile that works. But in real projects, size matters 👇 Problem: • Large images (1GB+) • Slow builds • Slow deployments Simple fixes: • Use lightweight base image (node:alpine / python:slim) • Copy only required files • Use layer caching (copy requirements first) • Avoid unnecessary packages 💡 Real-world impact: Smaller image = • Faster CI/CD • Faster deployments • Lower storage cost 📌 One-line takeaway: Optimized image = Faster everything ➡️ Tomorrow: Docker volumes (data persistence) #Docker #DevOps #Cloud #LearningInPublic