awsdynamodb

Design AI personas with real personality traits.

Migrating an On-premise MySQL to RDS MySQL and also inserting the migrated records into AWS DynamoDB using Lambda Function

Track Analyzer is the first mobile app providing high-level motion analysis for athletics balancing accuracy, speed, accessibility and portability. By using pose estimation AI models, the system provides immediate and accurate feedback on technical gestures without expensive hardware.

This project is a fully serverless URL shortener built entirely using the AWS Management Console without any external tools. It allows users to shorten long URLs and access them via custom short links.

A stereo-vision pipeline that detects speed humps and estimates their height in real time, using two cameras, YOLOv8, and OpenCV — deployed on AWS across a two-VPC architecture using EC2, VPC Peering, Application Load Balancer, Auto Scaling Groups, S3, DynamoDB, Lambda, Route 53, ACM, Security Groups, IAM, and CloudWatch.

Implemented An API Using NodeJs,ExpressJs and AWS DynamoDB

Production-ready serverless URL shortener built with AWS Lambda, API Gateway, and DynamoDB

This project is a fully serverless contact form, built using AWS Lambda (Python), API Gateway, and DynamoDB. The frontend is a simple HTML file that can be opened in any browser—no hosting or domain required. Form submissions are securely processed and stored in DynamoDB with robust validation and error handling.

Full DevOps pipeline around a Spring Boot API — Jenkins CI/CD, Terraform, Ansible, AWS EC2, Docker

AWS AI Stack is a ready-to-use, full-stack boilerplate for building serverless AI applications on AWS. This project provides a solid AWS foundation, allowing developers to integrate AI models via AWS Bedrock while ensuring data privacy and separation from model providers.

NoteCognition is a premium, local-first Markdown editor with real-time WebSocket synchronization across devices. It features offline-first architecture using IndexedDB and offers users the ability to deploy their own private backend on AWS with a single click, ensuring complete data ownership.

Built an event-driven file upload and processing system using AWS S3, Lambda, DynamoDB, IAM, and CloudWatch. Files uploaded to S3 automatically trigger Lambda for validation and metadata extraction, with results stored in DynamoDB and execution monitored through CloudWatch logs.

Serverless app: S3 + Lambda + Rekognition + DynamoDB + API Gateway (SAM) + static UI