📝 AWS Feedback System

A scalable feedback collection and sentiment-based analysis system built using AWS Lambda, API Gateway, DynamoDB, RDS (PostgreSQL), and a simple HTML dashboard for visualization.

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📌 Project Overview

This project allows users to submit feedback through a POST API, which stores the data in both Amazon RDS (PostgreSQL) and DynamoDB. A separate GET API retrieves the feedback data and displays it in an HTML table. It includes sentiment tagging (based on rating), monitoring with CloudWatch, and auditing with CloudTrail.

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✨ Features

•	✅ Submit feedback via POST API
•	✅ Store feedback in both RDS and DynamoDB
•	✅ Auto-tag sentiment based on rating (Positive/Negative/Neutral)
•	✅ Retrieve feedback with GET API
•	✅ Display feedback in a HTML dashboard
•	✅ Monitor performance & usage with CloudWatch
•	✅ Audit events with CloudTrail
•	✅ Alerts for Lambda errors using CloudWatch Alarms

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🛠️ Tech Stack

Component	Technology
Backend (Lambda)	Java 11 (Maven Project)
API Layer	Amazon API Gateway
Storage	DynamoDB, PostgreSQL (RDS)
Monitoring	Amazon CloudWatch
Auditing	AWS CloudTrail
Frontend	HTML + JavaScript (Fetch API)

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⚙️ Architecture

   ┌────────────┐
   │  Frontend  │  (HTML Dashboard)
   └─────┬──────┘
         │
    [GET Request]
         │
   ┌─────▼──────┐         ┌────────────┐
   │ API Gateway│ ─────→  │ Lambda GET │ ─────→ DynamoDB
   └─────┬──────┘         └────────────┘

         ▲
   [POST Request]
         │
   ┌─────▼──────┐         ┌────────────┐
   │ API Gateway│ ─────→  │ Lambda POST│ ─────→ DynamoDB + RDS
   └────────────┘         └────────────┘

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📊 CloudWatch Dashboard

Monitors: • Lambda Invocations • Errors & Duration • API Gateway Latency • DynamoDB Read/Write Capacity

Widgets include: • 📈 Lambda Invocations (Post & Get) • ⚠️ Error Count • ⏱️ Duration by Function • 🔥 Throttles • 🔍 DynamoDB Read/Write Ops • 🚨 Alarms Triggered Summary

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🔐 CloudTrail Integration

CloudTrail tracks: • API Gateway usage • Lambda executions • DynamoDB and RDS access logs • IAM role activities

Useful for auditing API usage and security analysis.

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📚 Learning Goals

•	Understand serverless application architecture on AWS
•	Work with both SQL and NoSQL data storage in one workflow
•	Implement basic sentiment logic with conditional logic in Lambda
•	Use CloudWatch for alerting, dashboards, and insights
•	Learn to audit and trace AWS usage using CloudTrail

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📦 Use Case / Real-World Scenario

This project simulates a real-world scenario where a business wants to: • 📥 Collect customer feedback from users about their experience, product, or service. • 🧠 Automatically derive sentiment from numerical ratings (positive, neutral, negative). • 🧾 Store data in both SQL and NoSQL formats for flexibility in analytics and performance: • DynamoDB for real-time fast retrieval • PostgreSQL for structured reporting and SQL-based querying • 📊 Display the feedback to administrators or managers in a web dashboard. • 🚨 Automatically alert engineers or DevOps teams in case of system errors or performance issues. • 🔍 Maintain a complete audit trail of all actions using CloudTrail for compliance or debugging.

This is especially relevant in industries like: • E-commerce platforms collecting product reviews • SaaS companies analyzing customer satisfaction • Educational platforms gathering student feedback • Mobile apps requesting in-app ratings and comments

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📝 Summary

This AWS-powered serverless project demonstrates how to build an end-to-end feedback collection and visualization system, combining: • 🔁 Real-time event-driven data flow • 📡 RESTful APIs with Lambda integration • 🛢️ Dual-database architecture (RDS + DynamoDB) • 📊 Monitoring and observability using AWS-native tools • 🔒 Secure, auditable, and scalable cloud architecture

Whether you’re a cloud engineer, developer, or student, this project offers practical insight into designing robust, scalable, and maintainable applications using modern AWS services.