📒 Expense Book

A serverless, full-stack expense tracker built entirely on AWS — no servers to manage, no infrastructure to maintain. Add expenses, track spending by category, and view monthly reports, all backed by a pay-per-use backend.

🔗 Live Demo: https://roshni-expense-book.netlify.app

💡 Why I built this

I wanted to understand serverless architecture from the ground up — not just read about Lambda and API Gateway, but actually wire them together, break things, read the CloudWatch logs, and fix them. This project is the result: a working expense tracker where every part of the stack — database, backend, API layer, and frontend — was built and debugged by hand.

⚙️ Architecture

┌─────────────┐ HTTPS ┌──────────────┐ Invoke ┌─────────────┐ Read/Write ┌──────────────┐ │ Frontend │ ─────────────▶ │ API Gateway │ ─────────────▶ │ Lambda │ ─────────────────▶ │ DynamoDB │ │ (HTML/CSS/JS)│ │ (REST API) │ │ (Python) │ │ (NoSQL) │ └─────────────┘ ◀───────────── └──────────────┘ ◀─────────────── └─────────────┘ ◀───────────────── └──────────────┘ Netlify Roshni stage 4 functions, Partition key: userId IAM execution role Sort key: expenseId

Request flow: the browser calls a REST endpoint → API Gateway (with Lambda proxy integration) forwards the raw event to the matching Lambda function → the function reads/writes DynamoDB using an IAM role scoped for that access → the response is serialized back through API Gateway to the browser, with CORS headers so the request isn't blocked cross-origin.

✨ Features

Add expenses with amount, category, description, and date

View all expenses in a clean, ledger-style list

Delete individual entries

Auto-generated monthly report with category-wise totals

Fully responsive UI, no frameworks — just HTML/CSS/JS talking directly to the API

Gateway Command Center — Real-Time Payment Operations Dashboard A monitoring and incident-response system for payment infrastructure, built to catch and resolve gateway failures before they become revenue loss.

What it does: Streams live transaction data, tracks failure rate and revenue-at-risk in real time, monitors multi-region edge cluster health (latency, status), and automatically triggers failover + incident triage when a region degrades. Includes a manual fault-injection simulator for stress-testing failure scenarios (rate limits, insufficient funds, fraud checks).

Key features: Live telemetry stream analytics, automated incident triage queue with action plans, multi-region failover, and a RAG-based semantic search over internal SLA/compliance policy documents with confidence scoring.

Stack: Python, real-time streaming, RAG pipeline, [your web framework].

Available for freelance work in fintech ops tooling, real-time systems, and RAG-based internal search