Why Choose This Project?

In the age of real-time analytics, IoT, and distributed systems, traditional server-based data processing can be costly and inefficient. This project demonstrates how to build a fully serverless, scalable, and event-driven pipeline using AWS Lambda and DynamoDB.

Ideal for students who want to explore cloud-native, event-driven architectures, and cost-effective serverless computing. It eliminates infrastructure overhead while enabling scalable ingestion and processing of data streams in real time.

Core Features

• Event-driven ingestion using AWS Lambda triggers

• Real-time data storage using DynamoDB

• Data validation and transformation logic in Lambda

• API Gateway for external data submissions

• Monitoring via AWS CloudWatch

• Role-based access using IAM

• Scalable architecture for any number of requests/events

• Optional: Integrate with Amazon S3, Kinesis, or SNS for extended capabilities

Technology Stack

Architecture Workflow

1. User or device sends data (e.g., IoT sensor data, app event logs) via a REST API.

2. AWS API Gateway receives the request and routes it to AWS Lambda.

3. Lambda function validates, parses, and transforms the data.

4. The processed data is then stored in DynamoDB.

5. CloudWatch logs and metrics monitor failures, latency, and execution time.

6. Optional: Add Lambda triggers for downstream processing (e.g., alerts via SNS, archiving to S3).

Security Best Practices

• Use IAM roles and policies to restrict access to Lambda and DynamoDB

• Validate incoming payloads in Lambda to avoid injection attacks

• Set rate limits and CORS rules in API Gateway

• Enable CloudWatch alarms for unusual activity or failures

Real-World Use Cases

• IoT sensor data ingestion

• User activity tracking in web/mobile apps

• Form submissions with audit logs

• Logging events from distributed apps

• Real-time analytics foundation

AWS Services Used