AWS Serverless Data Management Project

Author: Hina Atif

📌 Project Overview

This project demonstrates a serverless data management architecture using AWS services. It integrates relational and NoSQL databases with AWS Lambda for scalable, event-driven data operations.

AWS Services Used

AWS RDS (Relational Database Service)

AWS DynamoDB

AWS Lambda

AWS IAM

Architecture

The system uses AWS Lambda to interact with:

RDS for structured relational data

DynamoDB for fast NoSQL operations

This design enables scalable data processing without managing servers.

Why This Project Matters

This project demonstrates how modern cloud applications can:

• Use serverless compute for scalability
• Combine relational and NoSQL databases
• Reduce infrastructure management overhead
• Enable event-driven data workflows

These patterns are widely used in real-world cloud and DevOps environments.

Implementation Steps

Created an RDS instance with a managed database engine

Created a DynamoDB table with a primary key

Configured an IAM role with permissions for Lambda to access RDS and DynamoDB

Created an AWS Lambda function to:

Insert data

Fetch data

Interact with both RDS and DynamoDB

Tested Lambda functions using the AWS Management Console

Screenshots

Screenshots of the AWS resources and outputs are included in the repository.

Screenshots

1. DynamoDB Table Items
   

2. Lambda Function Code Logic
   

3. Lambda Test Response
   

4. MySQL Query Validation
   

5. RDS Connection Success
   

6. RDS Metrics Summary
   

📈 Learning Outcome

Understanding of serverless architecture

Hands-on experience with AWS relational and NoSQL databases

Lambda integration with multiple data sources

IAM role and permission management

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Future Improvements

API Gateway integration for RESTful endpoints

Infrastructure as Code (IaC) using Terraform

CI/CD automation for deployment