Python on AWS Scalable Backend Systems

AWS Lambda is one of the simplest ways to run code without managing servers. You just write your function, upload it, and AWS takes care of everything else — scaling, availability, and execution. It works on an event-driven model. That means your code runs only when something triggers it, like an API request, file upload to S3, or a database change. You’re not paying for idle time — only for actual execution. Lambda supports multiple languages like Python, Node.js, Java, and more. It’s widely used for building APIs, automation tasks, data processing, and backend services. In short, Lambda helps you focus on logic instead of infrastructure, making development faster and more efficient. #AWS #Lambda #CloudComputing #Serverless #DevOps #Programming #SoftwareDevelopment #Tech #Backend #Cloud

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

⚡ AWS Lambda — Run Code Without Managing Servers Tired of provisioning and maintaining servers? Meet AWS Lambda 👇 🔹 What is AWS Lambda? 👉 A serverless compute service by Amazon Web Services ✔ Run code without managing infrastructure ✔ Automatically scales ✔ Pay only for execution time 🔹 How It Works 1️⃣ Upload your code (Java, Python, Node.js, etc.) 2️⃣ Set a trigger (event) 3️⃣ Lambda executes your function 🔹 Common Triggers ✔ Amazon S3 → File upload ✔ Amazon API Gateway → HTTP requests ✔ Amazon SQS → Queue events ✔ Amazon SNS → Pub/Sub events 🔹 Use Cases ✔ Image processing ✔ Real-time file processing ✔ Backend APIs ✔ Event-driven microservices ✔ Scheduled jobs (cron) 🔹 Example (Spring Boot alternative — lightweight Java handler) public class LambdaHandler implements RequestHandler<String, String> { @Override public String handleRequest(String input, Context context) { return "Hello " + input; } } 🔹 Why Use Lambda? 🔥 No server management 🔥 Auto scaling 🔥 Cost efficient 🔥 Seamless AWS integration ⚠ Things to Watch ❗ Cold starts (especially in Java) ❗ Execution time limits ❗ Stateless design required 📌 Bottom Line Focus on code, not infrastructure — Lambda is the backbone of serverless architecture. #AWS #Lambda #Serverless #CloudComputing #Microservices #DevOps

As I continue to architect and scale distributed microservices and end-to-end data pipelines, I've discovered that the right stack is crucial for achieving low-latency, highly reliable solutions. Here’s an overview of the core technical stack I utilize to enhance performance and construct scalable enterprise systems: • Languages & Core Frameworks: Core logic is developed using Java, Python, and SQL, while robust services are built with Spring Boot, FastAPI, and Flask. • Cloud Architecture & Containers: Applications are deployed and scaled using AWS (EC2, S3, RDS, Lambda), Docker, and Kubernetes. • APIs & Event-Driven Messaging: Seamless service-to-service communication is orchestrated via REST APIs, gRPC, Kafka, and RabbitMQ. • Databases & Caching: High-volume data storage is managed and optimized across PostgreSQL, MySQL, MongoDB, and Redis. • DevOps, CI/CD & Observability: Deployments are automated with Jenkins and GitHub Actions, while system health is maintained using Prometheus, Grafana, and the ELK Stack. Tools are ultimately a means to an end. The real magic occurs when they are woven together—such as combining Spring Boot 3, Kafka, and AWS to build seamless microservices that process telemetry data without missing a beat. #TechStack #SoftwareEngineering #DataEngineering #CloudComputing #AWS #Python #Java #Kafka #Springboot #Kubernetes

🚀 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

🚀 Flask App Deployed on AWS Elastic Beanstalk (No CLI Required!) Excited to share my latest project where I deployed a Python Flask web application using AWS Elastic Beanstalk, entirely through the AWS Management Console ☁️ 🧑💻 Project by: Avdhoot Lad 🔧 Tech Stack: Python (Flask) Gunicorn (WSGI server) AWS Elastic Beanstalk AWS EC2 🏗️ Architecture (No CLI) User → EC2 → Flask App (Gunicorn) (Managed automatically by Elastic Beanstalk) ✨ Key Highlights: ✅ Simple deployment without any CLI commands ✅ AWS handles EC2 provisioning, environment setup, scaling & monitoring ✅ Production-ready setup with Gunicorn ✅ Easy updates via Upload and Deploy in console 📌 Learnings: How to deploy apps via AWS Console Understanding Elastic Beanstalk environment management Deploying production-ready Python applications 📸 Attached: Custom architecture diagram for No CLI deployment Next steps: 🔹 Add custom domain + HTTPS 🔹 Implement CI/CD pipeline #AWS #Flask #ElasticBeanstalk #CloudComputing #Python #DevOps #WebDevelopment #LearningByDoing

🚀 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

☁️ Java + AWS: Building Scalable Systems in the Cloud Most developers think scaling systems is about infrastructure. It’s not. It’s about architecture + decisions + the right stack — and that’s where Java + AWS shines. 🔧 What makes this combo so powerful? Java gives you: ✔ Stability ✔ Performance at scale ✔ Mature ecosystem (Spring, Quarkus, Micronaut) AWS gives you: ✔ Elastic infrastructure ✔ Managed services ✔ Event-driven architecture 🚀 Real-world patterns I see working 🔹 Serverless APIs (Java + Lambda) Handle requests without managing servers 🔹 Event-driven systems (SQS, SNS, EventBridge) Decouple services and scale independently 🔹 Microservices (Spring Boot + ECS/EKS) Flexible and production-ready 🧠 What most people ignore The real challenge is not deploying — it’s designing: Logging & tracing API contracts Data consistency Observability Cost optimization These decisions define whether your system scales… or breaks. 💡 Final Thought Java is not outdated. AWS is not just cloud. Together, they enable systems that are: ⚡ Scalable 🧠 Intelligent 🔗 Resilient 🚀 Production-ready #Java #AWS #CloudComputing #BackendDevelopment #SoftwareEngineering #Microservices #Serverless #CloudNative #Tech

Check out the example of AWS Lambda with Custom Runtime based on GraalVM Native Image (with version 25) using Spring Boot 4 with AWS Serverless Java Container, Spring JDBC, Hikari Connection Pool and Amazon Aurora DSQL database 👉 https://lnkd.in/ePQRJaxu If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories a star! Please also check out my website 👉 vkazulkin.com for more technical content and upcoming public speaking activities. Amazon Web Services (AWS) GraalVM #PostreSQL #Java #Serverless #Spring #SpringBoot

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