AWS Projects for Practice

Finding good AWS projects for hands-on practice has gotten complicated with all the tutorial sites, bootcamp projects, and YouTube walkthroughs flying around. As someone who built my entire cloud career on personal projects before landing my first AWS job, I learned everything there is to know about which projects actually build useful skills versus which ones are just busywork. Today, I will share it all with you.

Let me be clear about something upfront: reading documentation and watching videos only gets you about 30% of the way there. The other 70% comes from actually building things, breaking things, debugging things, and figuring out why your CloudFormation template is failing at 2 AM. These projects are designed to give you that hands-on experience in a meaningful and structured way.

I’ve organized these projects roughly from beginner to advanced, and each one builds on skills from the previous ones. Don’t skip ahead — the fundamentals matter.

Deploy a Static Website on Amazon S3

Probably should have led with this section, honestly. This is the project every AWS beginner should start with because it touches multiple services while remaining approachable. You’ll learn S3, CloudFront, Route 53, and ACM — and you’ll have something you can actually show people when you’re done.

Here’s what you’ll build: a static website hosted on S3, served through CloudFront CDN for global performance, with a custom domain from Route 53, and an SSL certificate from ACM for HTTPS. The whole thing costs pennies to run but teaches you fundamental AWS concepts that come up in every certification exam and job interview.

Step by step, here’s my recommended approach:

1. Create an S3 bucket with static website hosting enabled
2. Upload your HTML, CSS, and JavaScript files
3. Configure a bucket policy for access (use OAI with CloudFront for best security practice)
4. Create a CloudFront distribution pointing to your S3 bucket as origin
5. Request an SSL certificate from ACM in us-east-1 (required for CloudFront)
6. Set up a hosted zone in Route 53 and create an alias record to CloudFront
7. Test everything end-to-end with your custom domain

What you’ll actually learn from this project goes beyond the service configuration. You’ll understand how DNS resolution works with Route 53, how CDN caching behavior affects your site updates, how SSL/TLS termination works at the edge, and how S3 access control policies function. These are foundational concepts that apply to nearly every AWS architecture.

My recommendation: don’t just follow a tutorial step by step. Build it, then delete everything and build it again from memory. Then build it a third time using CloudFormation or Terraform. That triple-pass approach is how I cemented my understanding of these services, and it works far better than a single walkthrough.

Set Up a Virtual Server with EC2

EC2 is the backbone of AWS compute, and understanding how to launch, configure, and secure an instance is non-negotiable knowledge for any cloud professional. This project takes you through the full lifecycle of a virtual server from launch to production readiness.

Launch an EC2 instance with Amazon Linux 2 or Ubuntu, install a web server (Nginx or Apache), configure security groups to allow only necessary traffic, set up an Elastic IP for a static public address, and serve a simple web application. Then go further: create a custom AMI from your configured instance, set up Auto Scaling to handle traffic spikes, and put an Application Load Balancer in front for load distribution and health checking.

The security group configuration is where most beginners make their first mistakes — and where you learn the most valuable lessons. Don’t just open port 22 to 0.0.0.0/0 and call it done. Restrict SSH access to your specific IP address. Open only the ports your application requires. Understand the difference between security groups (stateful, instance-level) and NACLs (stateless, subnet-level). Set up separate security groups for your web tier and application tier. These networking fundamentals will serve you throughout your entire AWS career.

For bonus points, set up Session Manager from Systems Manager instead of using SSH directly. This eliminates the need for open port 22 entirely, which is a security best practice I follow in all production environments. It also provides audit logging of every session, which compliance teams love.

Build a Serverless Application with AWS Lambda

That’s what makes serverless projects endearing to us cloud practitioners — they force you to think differently about architecture. No servers to manage, no capacity to plan, no patches to apply, just pure application logic running on demand.

Build a REST API using Lambda, API Gateway, and DynamoDB. The classic CRUD application: create items, read items, update items, delete items. It sounds simple, but the learning packed into this single project is enormous and directly applicable to real-world applications.

You’ll learn:

• Lambda function creation in Python or Node.js, deployment packages, and testing with event payloads
• API Gateway configuration — REST API setup, resource methods, request/response mapping, and CORS configuration
• DynamoDB table design — partition keys, sort keys, secondary indexes, and fundamental access patterns
• IAM roles for Lambda execution with least-privilege permissions (don’t use the catch-all policy)
• CloudWatch Logs for debugging — reading Lambda logs is a skill you’ll use constantly
• Environment variables and configuration management for different stages (dev/staging/prod)

Once the basic CRUD API is working, level it up: add authentication with Cognito User Pools, implement input validation and proper error handling with appropriate HTTP status codes, set up API keys and usage plans for rate limiting, and add a DynamoDB stream that triggers a notification Lambda. Each addition teaches you a new service integration pattern that you’ll encounter in production environments.

I also recommend deploying this project using SAM (Serverless Application Model) or the Serverless Framework. Managing serverless infrastructure as code is essential for production workflows, and learning it now will save you headaches later.

Set Up a Relational Database Service (RDS) Instance

Most real-world applications need a relational database, and RDS is how AWS delivers managed database services. This project teaches you database provisioning, network security, backup management, high availability, and disaster recovery — all critical skills for any cloud professional.

Launch an RDS instance running MySQL or PostgreSQL (both are excellent choices — I personally lean toward PostgreSQL for new projects), configure it in a Multi-AZ deployment for high availability, set up automated backups with a sensible retention period, create a read replica for scaling read workloads, and connect it to your EC2 or Lambda application. The networking component here is absolutely critical: your database must be in a private subnet, accessible only from your application servers, never directly exposed to the internet.

I specifically recommend creating a database in a private subnet behind a NAT gateway, with a bastion host or Session Manager for administrative access. This configuration comes up in virtually every production environment and on every AWS certification exam. Understanding VPC networking, private vs public subnets, route tables, NAT gateways, and security group chaining through this hands-on project is vastly more effective than reading about them in documentation pages.

Practice these operational tasks as well: perform a manual snapshot, restore from that snapshot to a new instance, test a failover in your Multi-AZ setup, modify instance size (scale up/down), and enable enhanced monitoring. These are the day-to-day operations that cloud engineers perform regularly.

Create a Scalable Web Application with Elastic Beanstalk

Elastic Beanstalk abstracts away much of the infrastructure management while still giving you access to the underlying resources. It’s perfect for deploying web applications quickly when you want managed infrastructure without surrendering all control.

Deploy a web application using your preferred language (Node.js, Python, Java, or any supported platform), configure environment variables for application settings, set up an internal load balancer for traffic distribution, enable Auto Scaling with appropriate min/max instance counts, and configure a custom domain with HTTPS. Then experiment with blue-green deployments and rolling updates to understand zero-downtime deployment strategies.

What makes this project particularly valuable is understanding what Beanstalk creates under the hood. After deployment, go into the EC2 console and examine every resource it provisioned: the instances, the load balancer, the Auto Scaling group, the security groups, the CloudWatch alarms. Understanding this abstraction layer helps you make informed decisions about when Beanstalk is the right tool and when you need to go lower-level with direct EC2 or container-based deployments.

Also explore Beanstalk’s .ebextensions configuration files. They let you customize the underlying infrastructure and install additional packages, configure Nginx, and set up log rotation. This is where Beanstalk transitions from a simple deployment tool to a genuinely powerful platform.

Build a CI/CD Pipeline with CodePipeline

DevOps skills are essential for any modern AWS professional, and building a CI/CD pipeline from scratch is the best way to learn them practically. This project ties together source control, automated testing, artifact building, and deployment into a cohesive automated workflow.

Set up CodePipeline to pull code from a GitHub or CodeCommit repository, run tests and build artifacts with CodeBuild, and deploy to Elastic Beanstalk, ECS, or Lambda. Every code push triggers the pipeline automatically, running your full test suite before deployment.

You’ll learn about build specifications (buildspec.yml), test automation strategies, deployment configurations, artifact management in S3, and rollback procedures when deployments fail. Configure SNS notifications so you get alerts when pipeline stages fail — this is a practice I follow in every production pipeline.

I suggest making the pipeline intentionally fail at some point during your learning process — push code that breaks tests. Watch how the pipeline handles the failure. Check the CodeBuild logs to understand the error. Then fix the code and watch the pipeline succeed on the next run. Understanding failure modes is just as important as understanding happy paths, and it’s something interviewers often ask about.

Implement Monitoring with CloudWatch

Monitoring is something every AWS professional needs to understand deeply, and this project teaches you how to build comprehensive visibility into your infrastructure and applications.

Set up CloudWatch dashboards that visualize key metrics, create custom metrics from your application code, configure alarms with SNS notifications for critical thresholds, and set up centralized log aggregation with CloudWatch Logs Insights. Apply these monitoring capabilities to one or more of your earlier projects — monitor your Lambda function invocations, your EC2 CPU utilization, your RDS connection counts, and your API Gateway request rates.

The real learning here is in alarm configuration: what thresholds make sense for your application? What’s the right evaluation period to avoid false alarms? How do you distinguish between a transient spike and a genuine issue? How do you avoid alarm fatigue while still catching real problems? These are decisions you’ll make daily in a production AWS environment, and practicing them now builds invaluable judgment.

Take it further: set up a CloudWatch composite alarm that only fires when multiple conditions are met simultaneously, configure anomaly detection for metrics where static thresholds don’t make sense, and create a CloudWatch Logs metric filter that generates alerts based on specific log patterns like error messages or slow response times.

Infrastructure as Code with CloudFormation

Take any project from above and rebuild it entirely in CloudFormation. This is the project that separates AWS users from AWS professionals. If you can describe your infrastructure in code, you can version it, review it, test it, replicate it, and deploy it consistently across environments.

Start simple — a VPC template with public and private subnets, an internet gateway, NAT gateway, and security groups. Then build up to a full application stack with networking, compute, database, and monitoring resources all defined in a single template or, better yet, nested templates that separate concerns logically.

Learn CloudFormation’s key features: parameters for customization, mappings for environment-specific values, conditions for optional resources, outputs for cross-stack references, and intrinsic functions like !Ref, !Sub, and !GetAtt. These features enable templates that are reusable across environments and accounts.

This project alone will give you skills that employers actively seek. Every job posting I see mentions Infrastructure as Code, and CloudFormation fluency demonstrates that you can manage AWS at a professional level. If you want to go even further, learn Terraform as well — it’s the most popular multi-cloud IaC tool and pairs beautifully with your CloudFormation knowledge.

Final Thoughts

The best AWS project is the one you actually complete and push to GitHub. Start with the S3 static website, work your way up to serverless applications, CI/CD pipelines, and monitoring dashboards, and document everything along the way. Each project builds on the previous one, and by the time you’ve completed five or six of these, you’ll have both the technical skills and the visible portfolio to compete for real AWS positions. Stop consuming tutorials and start building things. That’s the real secret to learning AWS, and it’s exactly what hiring managers are looking for when they review candidates.