Optimize AWS Lambda deployment packages for faster cold starts

Project: Terraform-based AWS Lambda + EventBridge + SNS Daily Reminder I recently built and deployed a fully automated daily reminder system on AWS using Terraform, Python, and EventBridge. The setup provisions all resources — including Lambda functions, IAM roles, SNS topics, and EventBridge schedules — entirely as code. Every day at 2:30 PM Toronto time, EventBridge triggers the Lambda function, which publishes notifications through Amazon SNS (SMS). Key highlights: • Infrastructure as Code with Terraform • Event-driven automation using CloudWatch/EventBridge • Secure IAM policies for least-privilege execution • Serverless Python function deployment • End-to-end automated SMS notifications 🔗 GitHub Repository: https://lnkd.in/eFyzfEvT This project showcases how easily daily operational workflows can be automated with serverless + IaC, reducing manual overhead while keeping infrastructure reproducible and scalable. #Terraform #AWS #DevOps #Lambda #EventBridge #SNS #CloudAutomation #InfrastructureAsCode #Serverless #Python

🧩 Task 2: Deploying a Python App using AWS ECR & ECS (Fargate) Today’s task focused on containerizing and deploying a simple Flask-based CRUD application using AWS Elastic Container Registry (ECR) and Elastic Container Service (ECS) with Fargate launch type. 🧱 Steps Performed: 1️⃣ Built a Docker image for the Flask application. 2️⃣ Tested the container locally on port 5000 to ensure functionality. 3️⃣ Created a private ECR repository named vijayproject. 4️⃣ Tagged and pushed the image (myapp:latest) to Amazon ECR. 5️⃣ Defined an ECS Task Definition referencing the ECR image. 6️⃣ Allocated 1 vCPU and 2GB Memory, with port mapping 5000:5000. 7️⃣ Added a health check endpoint to monitor container status. 8️⃣ Deployed the task on AWS Fargate with a public IP (no Load Balancer due to Free Tier setup). 9️⃣ Verified successful application deployment within ECS. 🚀 🎯 Outcome: This task provided hands-on experience in building, containerizing, and deploying an application using AWS-managed services — a key step in understanding real-world DevOps and Cloud deployment pipelines. #Task2 #DevOps #AWS #ECR #ECS #Docker #Fargate #Python #Flask #CloudDeployment #Containerization #AWSProjects

🚀 Infrastructure as Code Made Easy: AWS CDK Edition If you're tired of writing YAML configs and CloudFormation templates manually, AWS-CDK is a game-changer for cloud developers! Here's why AWS-CDK is revolutionizing infrastructure development: ✅ Write Infrastructure with Familiar Languages Use TypeScript, Python, Java, or .NET instead of YAML/JSON. Your infrastructure code feels like real programming! ✅ Reusable Constructs = DRY Infrastructure No more copy-pasting CloudFormation templates. Create composable, tested components that work across projects. ✅ Automated CI/CD Pipelines Define your entire deployment pipeline in code. Easy version control, code reviews, and rollbacks. ✅ Security Best Practices Built-In CDK enforces security configurations by design—encryption, IAM policies, VPC configurations right out of the box. 💼 Real-World Use Cases: • Deploy serverless microservices with Lambda + API Gateway in minutes • Set up multi-region infrastructure for global applications • Quickly spin up databases, queues, and event-driven architectures ⚡ The Result? Faster cloud development, fewer manual errors, and infrastructure that scales with your business. Have you tried AWS CDK? What's been your experience? Drop your thoughts in the comments! 👇 #AWS #CDK #Infrastructure #CloudDevelopment #DevOps #IaC

Part 4 🧠 — Building AutoChef: A GenAI Recipe Platform from Scratch - Implementing Infrastructure as Code (AWS CDK) This week i have moved AutoChef from code to cloud with AWS CDK What I built:🚀 End-to-end IaaC with AWS CDK: VPC, subnets, security groups, and ECR repositories Application Load Balancer with path-based routing ECS Fargate cluster with Java (Spring Boot) & Python (FastAPI) microservices Service Discovery (Cloud Map) for robust, DNS-based inter-service communication Key Learnings & Issues Faced: 🧩 Service Discovery in ECS: - Moving from “localhost” to DNS-based service names (python-service.autochef.local) was essential for scalable, resilient microservices. - Debugging DNS, VPC, and Route 53 associations was a real-world lesson in cloud networking. 🐛 The “LLM_DOWN” Mystery: Despite perfect infra and healthy containers, Java couldn’t reach Python — but after deep debugging, the real blocker turned out to be downstream. AWS Bedrock started returning “Operation not allowed” for all API calls, even with AdministratorAccess. The root cause is still under investigation with AWS Support . 🔎 Debugging in the Cloud: Used AWS Console and CLI to trace every layer: ECS task definitions, Cloud Map registrations, Route 53 DNS records, VPC DNS settings, and IAM roles. Rebuilt and redeployed Docker images, forced ECS task restarts, and validated every config — proving that real-world cloud engineering is as much about detective work as it is about code. What i understood is that building cloud-native GenAI systems isn’t just about model integration — it’s about engineering reliability, security, and observability into every layer across distributed microservices and cloud platforms. #BuildInPublic #AWS #AWSCdk #ECS #Fargate #InfrastructureAsCode #DevOps #Microservices #GenAI #SoftwareEngineering #CloudComputing #Bedrock #FastAPI #SpringBoot

𝗗𝗲𝗽𝗹𝗼𝘆 𝗙𝗹𝗮𝘀𝗸 𝗔𝗽𝗽 𝗼𝗻 𝗔𝗪𝗦 𝗘𝗖𝗦 𝗙𝗮𝗿𝗴𝗮𝘁𝗲 (𝗣𝗮𝗿𝘁 𝟱 𝗼𝗳 𝗼𝘂𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗝𝗼𝘂𝗿𝗻𝗲𝘆) Today, we're going deploy a containerized Flask application on AWS ECS Fargate using ECR, Load Balancer, Security Groups, and Task Definitions. Key learning: - Fargate is fully serverless: no EC2 instance mgmt 🙌 Go and deploy your ECS application. Blog link is in the comment section. Happy & Fun Learning Chetan Mohod #AWS #DevOps #ECS #Fargate #CloudComputing #Docker #Python #Flask #Hashnode

🐳 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗗𝗼𝗰𝗸𝗲𝗿 𝗮𝘀 𝗮 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: 𝗪𝗵𝗮𝘁 𝗜 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 𝗧𝗵𝗶𝘀 𝗪𝗲𝗲𝗸 This week, Docker really tested me — in the best way possible. While building my multi-container app (Flask + Redis), I ran into several errors that completely stopped my containers from running. At first it was frustrating… but it quickly became one of the most valuable parts of the learning process. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗶𝘀𝘀𝘂𝗲𝘀 𝗜 𝗵𝗶𝘁: 🔹 A typo in Python: _name_ instead of __name__ 🔹 YAML formatting errors in docker-compose.yml 🔹 “Additional properties not allowed” validation messages 🔹 Containers restarting because of small mistakes in my code 🔹 Redis + Flask not connecting until I understood Docker networking Every error forced me to slow down, read the logs, understand what Docker was trying to tell me, and fix the root cause instead of guessing. 💡 𝗪𝗵𝗮𝘁 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗧𝗮𝘂𝗴𝗵𝘁 𝗠𝗲 ✔ Logs are your best friend docker-compose logs or checking individual container logs instantly helped me pinpoint issues. ✔ YAML is unforgiving One wrong indent or character can break everything — great practice for Kubernetes and CI/CD later. ✔ Containers fail fast (and that’s a good thing) It makes problems visible immediately instead of failing in production. ✔ Small mistakes are part of the journey Every “error” made a core concept click for me — images, containers, networking, ports, and volume behavior. ✔ Real learning happens when things break Fixing issues teaches more than any tutorial ever could. 🎯 𝗪𝗵𝘆 𝗜’𝗺 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗧𝗵𝗶𝘀 Because DevOps isn’t about never making mistakes — it’s about improving your problem-solving skills with every error message. And honestly, I’m starting to enjoy debugging more than I expected. Step by step, this journey is building my confidence and preparing me for AWS, Kubernetes, CI/CD pipelines, and Terraform. #DevOps #Docker #DockerCompose #Containers #Flask #Python #Redis #LearningInPublic #TechJourney #DevOpsEngineer #Debugging #CloudEngineering #CloudComputing #CICD #InfrastructureAsCode #AWS #Kubernetes #Terraform #Upskilling #100DaysOfCloud

🚀 AWS BugBust: ML-Powered Bug Prioritization for Efficient Code Reviews AWS BugBust leverages Amazon CodeGuru's machine learning models to automatically rank bugs by severity and impact, helping development teams focus their debugging efforts where they matter most. ✅ Technical Breakdown: • Integrates CodeGuru Reviewer's ML models trained on 100,000+ Amazon code reviews • Automatically categorizes bugs into Security, Performance, and Code Quality buckets • Assigns severity scores (Critical, High, Medium, Low) based on potential production impact • Supports automated scanning for Java and Python repositories in GitHub, Bitbucket, and CodeCommit • Provides fix recommendations with code snippets for common vulnerability patterns 📌 Real-World Impact: • Reduces mean time to resolution (MTTR) by focusing developers on critical bugs first • Prevents security vulnerabilities from reaching production through early detection • Eliminates analysis paralysis when dealing with large technical debt backlogs • Gamification elements increase bug fix completion rates by up to 30% • Provides quantifiable metrics for code quality improvements during sprints 💡Pro Tip: Configure BugBust events before major releases to create focused bug-fixing sprints. Set custom point values for different bug categories to align with your team's priorities - assign higher points to security vulnerabilities to incentivize their immediate resolution. Learn more about: https://lnkd.in/eGFFdzzM #AWS #DevOps #CodeQuality #MachineLearning #CloudComputing #SoftwareEngineering

Here's a look under the hood of my event-driven, serverless news aggregator. This video demonstrates the final product of an architecture powered entirely by AWS managed services. You're seeing the user-facing frontend hosted on S3 interacting in real-time with an API built on Lambda and API Gateway. The data ingestion is fully automated via EventBridge, feeding a DynamoDB table. It's a practical example of a modern, decoupled cloud application. Happy to discuss the architecture or any part of the build process in the comments! #AWSArchitecture #Serverless #Lambda #DynamoDB #CloudNative #DevOps #Python

Working with microservices has taught me that performance tuning isn’t just about code — it’s about how you package and deploy it. Over the past week, I experimented with Docker multi-stage builds, resource limits, and container health checks across a full stack of Python and Node.js services. ✅ Reduced build size ✅ Lowered CPU usage by 60% ✅ Achieved faster deployments on local and AWS environments Learning never stops — especially when you’re building smarter, leaner systems! #Microservices #Docker #LearningByDoing #CloudEngineering #FastAPI #NodeJS #AWS

Happy to share that my latest blog post, "Installing Packages from a Private PyPI Repository on Databricks Clusters," is now live! This post walks through how to connect Databricks clusters to private Python package repositories, focusing on Azure DevOps Artifacts, but with an approach that applies to any managed artifact service. It’s based on real-world scenarios where teams need secure, governed access to internal libraries and approved third-party dependencies. If you're working in an enterprise environment and want to streamline how your Databricks clusters access private PyPI feeds, while ensuring compliance and maintainability, this post is for you! Check it out here: https://lnkd.in/dZMWMDE2 I’d love to hear your thoughts.