Serverless Image Processing Pipeline on AWS Lambda
Upload photos from a React app and let AWS Lambda resize, watermark and tag them automatically, with no servers to manage.
Representative screens. Your project is built to the listed features after you order.
Need your guide to approve it first?
Download the two-page synopsis and abstract, free.
Compiled for you, not resold
Every codebase is compiled to order, configured with its own database migrations and bundled with a report written to your university’s format, so your submission never matches anyone else’s. Deliverables reach your vault within 10 to 12 working days, depending on demand and how much customisation you ask for.
Problem Statement & Abstract
Serverless computing lets you run code only when something happens and pay only for the milliseconds it runs. This project builds a complete event-driven image pipeline on AWS without a single server to patch or scale.
Users upload photos from a React web app using pre-signed S3 URLs, so files go straight to S3 and never pass through an application server. Each upload triggers an AWS Lambda function written in Python that creates thumbnails in three sizes, adds a watermark, strips location data from the photo for privacy and calls Amazon Rekognition to detect labels such as "beach" or "car". Results are saved to DynamoDB and the gallery updates automatically.
API Gateway exposes a small REST API for listing, searching by label and deleting images. Failed processing is retried and sent to a dead-letter queue, and CloudWatch dashboards show invocations, duration and errors. The report explains serverless pricing with real numbers from the demo, and an AWS SAM template deploys or deletes the whole stack with one command.
Key Features
- Direct-to-S3 uploads with pre-signed URLs
- Event-driven Lambda processing on every upload
- Thumbnails in three sizes plus watermarking
- Privacy: GPS/EXIF location data stripped from photos
- Automatic tagging with Amazon Rekognition
- Search the gallery by detected label
- Retries with an SQS dead-letter queue for failures
- One-command deploy and delete with AWS SAM
Suitable For
Everything included
+ GST • about $107 USD
Added to your cart
- Delivered in 10 to 12 working days
- Free revisions for 30 days while your guide reviews
- Report written to your university’s format