AWS Cloud Projects for Engineers: 5 Real-World Projects

What we’re building, customer requirement?

S3, CloudFront, AWS Certificate Manager, API Gateway, IAM, Lambda, SES, DynamoDB.

Set up an S3 bucket (blocked public), serve it through CloudFront, attach an ACM certificate, map a custom domain in Route 53, and verify website with domain name.

Create POST /epicreads_resource in API Gateway, enable CORS (OPTIONS + POST), implement a Lambda that sends email via SES, return proper CORS headers, and test with curl and the webpage.

Create a DynamoDB table (PK: id, on-demand), add an inline policy for dynamodb:PutItem, update the Lambda to store submissions, add basic validation, and confirm items and logs.

In this short briefing, I have explained the next assignment.

Disable/delete CloudFront, delete API/Lambda/DynamoDB/IAM/ACM/SES artifacts, and review costs and production tips (restrict origins, WAF rate limits, SES sandbox).

In this lecture, I’ll show you how this project is inspired by real-world customer environments.

We’ll walk through how real IoT devices send data from machines to AWS IoT Greengrass, stream it via Kinesis, store it in DynamoDB, and integrate it with enterprise data from SAP. You’ll also see how we layer this architecture (Edge → Data Lake → Processing → Applications), deploy applications using CI/CD pipelines, and secure them with authentication, analytics, and monitoring tools.

This session helps you understand how even small course projects map to real industry solutions, preparing you to think like a cloud architect—not just follow steps.

In this lecture, we’ll walk through the project overview and objectives for our IoT-based printer monitoring system. You’ll learn how to break down the customer requirements, understand key concepts like handling large data streams, using device-specific thresholds, and triggering anomaly alerts.

I’ll also show how to use tools like ChatGPT to clarify requirements—helping you analyze and fully understand complex project briefs before starting the implementation.

In this lecture, I’ll walk you through how to think like a cloud architect before jumping into implementation. You’ll see how to analyze the project overview, derive the problem statement, explore multiple solution approaches (AWS managed services vs custom development), and design a high-level architecture.

This session will help you build the habit of creating your own project document before coding—just like in real-world enterprise environments.

n this lecture, we’ll begin implementing our solution. Building on the project document we created earlier, you’ll learn how to set up your development environment, verify AWS CLI access, and start creating the core components—starting with a DynamoDB table for printer profiles.

You’ll also see how to use AWS CLI and documentation effectively (and tools like ChatGPT) to understand commands and build real-world skills while implementing this project.

In this lecture, we’ll walk through the complete Lambda code for detecting printer anomalies, then perform end-to-end testing. You’ll see how the Lambda processes IoT data, compares values against thresholds from DynamoDB, updates event counts, and publishes predictions.

We’ll also test it using a Python script that sends simulated sensor data to AWS IoT, verify updates in DynamoDB, and confirm anomaly events are triggered correctly—completing Assignment #2.

In this lecture, we’ll shift from data ingestion to data visualization. You’ll learn how real-world IoT projects move beyond alerts to dashboards that display printer metrics, thresholds, and anomaly counts at scale.

We’ll explore common AWS visualization patterns (QuickSight, Grafana, OpenSearch) and plan our approach: building a custom real-time dashboard using React + TypeScript, powered by an API Gateway + Lambda backend that fetches data from DynamoDB.

In this lecture, we’ll build the backend for our printer dashboard as part of Assignment #3. You’ll create a Lambda function (Node.js) to fetch data from DynamoDB and expose it via an API Gateway GET endpoint using a CloudFormation template.

We’ll test the endpoint with curl and browsers, verify API → Lambda → DynamoDB integration, and fix common issues (like reserved keywords in DynamoDB). This backend will power our upcoming React + TypeScript dashboard.

In this lecture, we’ll build the React + TypeScript dashboard for visualizing printer IoT data. You’ll set up a React project, connect it to your API Gateway endpoint, fetch data from DynamoDB, and display printer metrics (thresholds, event counts, out-of-bound counts) in a clean interface.

You’ll also learn how to structure your React app, use Axios for API calls, and verify data in real time—preparing the dashboard for deployment in the next assignment.

Learn a repeatable way to troubleshoot with AI. We’ll diagnose why IoT → IoT Rule → Lambda → DynamoDB stops updating, craft effective ChatGPT prompts with full context, read CloudWatch logs, fix common issues (ARN typos, reserved keys), and use a clear question template for support.

Build a real 3-tier app: Next.js UI, Node/Express API, MySQL RDS. Create VPC, subnets, SGs, wire auth & reviews, and run end-to-end with SSR.