Triggering AWS Lambda with S3 Event Notifications

Just wrapped up my latest cloud architecture project: A completely Event-Driven Serverless Automation Pipeline! 🚀☁️ I’ve been diving deep into modern cloud architecture, and I wanted to build something that solves real-world problems: manual data processing and expensive idle servers. So, I architected and deployed a "Zero-Maintenance" Serverless Pipeline using AWS. Here is what the architecture looks like under the hood: 🔹 Event-Driven: Dropping a file into an AWS S3 bucket instantly triggers the correct pipeline workflow based on the file type (logs, images, or data). 🔹 Microservices: Engineered Python-based AWS Lambda functions to handle regex log analysis, high-quality image resizing, and strict JSON/CSV schema validation. 🔹 Infrastructure as Code (IaC): The entire environment is fully reproducible, provisioned programmatically using Terraform with strict least-privilege IAM roles. 🔹 Observability: Centralized results in DynamoDB, with custom CloudWatch metric filters that trigger SNS email alerts if any pipeline throttles or errors out. 🔹 CI/CD: Automated testing and linting pipelines built with GitHub Actions to ensure code quality before deployment. Building this reinforced how powerful the "serverless mindset" is. You only pay for the exact milliseconds your code runs, and the infrastructure scales infinitely on demand. I even built a custom dark-themed dashboard to monitor the mock data flow! 📊 I've made the entire Terraform configuration, Python source code, and CI/CD pipelines public. Check out the repository here: [Insert your GitHub Repo Link] I would love to hear from the community — what is your favorite use case for event-driven architecture? Let me know below! 👇 #AWS #Serverless #CloudComputing #Terraform #Python #DevOps #SoftwareEngineering #Architecture #GitHubActions

🚀 Excited to share my latest project: **End-to-End MLOps Pipeline for Vehicle Insurance Prediction** I built a **production-ready Machine Learning system** that goes beyond model training and covers the complete lifecycle — from **data ingestion to cloud deployment**. 🔧 This project combines **Machine Learning + Software Engineering (SDE) + DevOps practices** to simulate a real-world industry setup. ### 💡 Key Highlights: * 🔄 End-to-End ML Pipeline (Data Ingestion → Validation → Training → Deployment) * ☁️ Cloud Integration using AWS (S3, EC2, ECR, IAM) * ⚙️ Automated CI/CD pipeline using GitHub Actions * 🐳 Containerization using Docker * 🗄️ Data storage using MongoDB Atlas * 🚀 Deployment on EC2 with live API endpoints ### 💼 What I focused on: * Writing **production-grade, modular code** * Implementing **SDE best practices** (logging, exception handling, clean architecture) * Automating workflows with **CI/CD** * Building a **scalable & maintainable ML system** 🌐 **Live Demo:** https://lnkd.in/gKixJXbA GitHub repo:- https://lnkd.in/g4nu8XAg This project helped me gain hands-on experience in: 👉 MLOps pipelines 👉 Backend + DevOps integration 👉 Cloud deployment on AWS 📌 Tech Stack: Python | FastAPI | Scikit-learn | Docker | AWS | GitHub Actions | MongoDB I’d love to hear your feedback and suggestions! 🙌 #MLOps #MachineLearning #AWS #Docker #CI_CD #SoftwareEngineering #DataScience #FastAPI #DevOps

👉 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

🚀 Demystifying AWS Lambda – The Power of Serverless Computing AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. It automatically scales based on demand and charges only for the compute time you use. 🔑 Key Concepts to Know: Serverless → Focus on writing code while AWS manages infrastructure. Event-driven → Functions run in response to events (S3 uploads, DB changes, API calls). Function as a Service (FaaS) → Execute small, independent functions on demand. Function → Your business logic packaged with configuration. Runtime → Execution environment (Python, Node.js, Java, .NET, etc.). Handler → Entry point for your function (filename.method_name). Event → JSON input representing the trigger data. Context → Runtime info (request ID, memory, timeout). Trigger → AWS service/resource that invokes your function (API Gateway, S3, DynamoDB, CloudWatch). 🖼️ Hands‑on with AWS Lambda + S3: Building an Image Resizer I recently walked through a practical lab where AWS Lambda automatically resizes images uploaded to S3. Here’s the workflow 👇 🔧 Step‑by‑Step Setup Step 1: Create S3 Buckets Source bucket → incoming uploads Destination bucket → resized thumbnails (region: us-east-1) Step 2: Create Lambda Function Name: ImageResizerFunction Runtime: Python 3.12 Architecture: x86_64 Step 3: Add Pillow Library via Layer Attach public ARN: arn:aws:lambda:us-east-1:770693421928:layer:Klayers-p312-Pillow:4 Step 4: Grant IAM Permissions Attach AmazonS3FullAccess (lab use; production should be restricted). Step 5: Write the Code Lambda downloads the image, resizes to 128×128 thumbnail, and uploads to the destination bucket. Step 6: Configure S3 Trigger Trigger on All object create events in source bucket. Add suffix .jpg to avoid non‑image files. Step 7: Test It! Upload a large .jpg to the source bucket. Within seconds, a resized thumbnail appears in the destination bucket. #AWS #Lambda #Serverless #CloudComputing #DevOps #FaaS

My first time sharing anything on LinkedIn. I recently built a small end-to-end data engineering side project on AWS to better understand how realtime ingestion, batch transformation, and analytics layers fit together in practice. The project includes: API Gateway, SNS, and SQS for event ingestion and decoupled delivery Lambda for realtime processing S3 for curated and transformed storage Glue for batch ETL Glue Catalog and Athena for analytics AWS CDK and Python for infrastructure and implementation One thing I’ve wanted to do for a while is build more end-to-end data engineering projects to broaden my understanding beyond small isolated pieces. This side project was a chance to connect ingestion, transformation, storage, and analytics into one pipeline. Coding agents also made it easier to iterate quickly, test ideas faster, and spend more time understanding the system design instead of getting stuck on setup friction. It’s a learning/portfolio project rather than a production system, but it was a useful exercise in thinking through architecture, cloud services, and data flow together. GitHub: https://lnkd.in/g2nxN9j2 #FirstPost #DataEngineering #AWS #Python #ETL #AWSLambda #AWSCDK #S3 #Glue #Athena

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

🚀 Week 8 of my 90-day Cloud Data Engineering journey — this week I stopped building pipelines and started shipping them like a production engineer. Here's what I built 👇 🐳 Multi-Stage Docker Build Packaged my ETL pipeline into a Docker image — 117MB instead of 800MB+ using multi-stage builds. Same container runs identically on my laptop, GitHub's servers, and GCP. ⚙️ GitHub Actions CI/CD — 3 automated jobs on every git push: → Lint with ruff + 12 pytest unit tests (86% coverage) → Multi-stage Docker build + push to GCP Artifact Registry → Terraform infrastructure plan Fully automated. Under 2 minutes. Zero manual steps. 🔐 Workload Identity Federation Zero JSON keys. Zero stored passwords. GitHub authenticates to GCP using short-lived tokens that expire when the job ends. Production security standard. 🏗️ Terraform — Infrastructure as Code Entire GCP infrastructure defined in reusable Terraform modules — GCS bucket, BigQuery dataset, partitioned tables, Cloud Monitoring dashboards and alerts. One command rebuilds everything from scratch. The biggest mindset shift this week: Infrastructure is just code. Write it, review it, test it, version control it — exactly like application code. 8 weeks down. Week 9 already in progress. 💪 🔗 https://lnkd.in/gscwH65N #CloudDataEngineering #GCP #Docker #Terraform #GitHubActions #DataOps #CICD #Python #DevOps #OpenToWork #DataEngineering #100DaysOfCode

🚀 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

✅ 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

🚀 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