Top 7 AWS Projects for Beginners

Summary:

• Seven hands-on AWS projects for beginners that build real cloud engineering skills while staying within Free Tier limits.
• Each project includes architecture guidance, code snippets, cost estimates, security considerations, and difficulty ratings.
• Learn foundational services like S3, Lambda, API Gateway, DynamoDB, EC2, and IAM through practical implementation.
• Comparison table maps projects to skills learned, services used, and estimated monthly costs.
• Complete CloudFormation starter template provided for the serverless contact form project.

Building production-grade cloud skills requires more than reading documentation. The fastest path to AWS proficiency runs through deliberate practice with real projects that mirror enterprise patterns. Whether you are preparing for cloud engineering interviews or transitioning from on-premises infrastructure, these seven AWS projects for beginners provide the scaffolding to develop genuine expertise. Each project targets specific service combinations, reinforces security best practices, and keeps your monthly bill at zero through strategic Free Tier usage.

The following transition diagram illustrates how these seven projects progressively build upon each other. It starts with static hosting fundamentals and advances toward event-driven architectures.

Project 1: Static website hosting with AWS S3 Free Tier

Static website hosting with AWS S3 Free Tier represents the ideal entry point for cloud beginners because it requires zero server management while teaching fundamental concepts like bucket policies, content delivery, and DNS configuration. S3 provides eleven nines of durability for stored objects, meaning your website assets are protected against data loss across multiple availability zones within a region. This project introduces you to the AWS Console, CLI basics, and the principle of least privilege through bucket policy configuration.

The architecture remains intentionally simple. You create a single S3 bucket configured for static website hosting, upload HTML, CSS, and JavaScript files, then configure a bucket policy that allows public read access only to website content. For production scenarios, you would front this with CloudFront for HTTPS and edge caching. The base implementation teaches core concepts without added complexity.

Implementation steps and cost analysis

Begin by creating a bucket with a globally unique name matching your intended domain. Enable static website hosting under the bucket properties, specifying index.html as your default document.

The critical security step involves two parts: First, you must explicitly edit the bucket’s permissions to turn off “Block all public access” (which AWS enables by default). Second, craft a bucket policy that grants s3:GetObject permission to the principal wildcard (*) while restricting the resource ARN strictly to your specific bucket path (arn:aws:s3:::your-bucket-name/*).

• Free Tier allocation: 5GB storage, 20,000 GET requests, 2,000 PUT requests monthly
• Estimated cost: $0.00 for portfolios under 5GB with moderate traffic
• Skills learned: S3 bucket configuration, IAM policies, DNS basics
• Difficulty rating: 1/5

Security considerations center on avoiding overly permissive bucket policies. Never grant s3:* actions or allow access to bucket listing operations. The AWS S3 documentation on website permissions provides policy templates that follow least-privilege principles. With static hosting fundamentals established, the next project introduces compute resources through EC2 instance deployment.

Project 2: Deploy a simple web app on EC2 with custom domain

Moving from managed storage to compute infrastructure, deploying a simple web app on EC2 with a custom domain teaches server provisioning, security group configuration, and the networking fundamentals that underpin all AWS architectures. EC2 instances provide the flexibility to run any application stack, making this knowledge transferable across frameworks and languages. Understanding EC2 also clarifies why serverless alternatives exist and when each approach fits.

This project involves launching a t2.micro or t3.micro instance within the Free Tier, installing a web server like Nginx, deploying application code, and configuring Route 53 for DNS resolution. The architecture introduces Virtual Private Cloud concepts including subnets, internet gateways, and security groups that function as stateful firewalls.

Security group configuration and SSH access

Security groups act as virtual firewalls controlling inbound and outbound traffic at the instance level. For a web server, you need inbound rules allowing TCP port 80 for HTTP, port 443 for HTTPS, and port 22 for SSH access restricted to your IP address. The outbound rule typically allows all traffic to enable package downloads and external API calls.

SSH key management deserves careful attention. Generate a new key pair during instance launch, store the private key securely, and never commit it to version control. For team environments, consider AWS Systems Manager Session Manager as an alternative that eliminates SSH key distribution entirely. The following code snippet demonstrates connecting to your instance and installing Nginx:

ssh -i "your-key.pem" ec2-user@your-instance-public-ip
sudo dnf update -y
sudo dnf install nginx -y
sudo systemctl start nginx
sudo systemctl enable nginx

• Free Tier allocation: 750 hours monthly of t2.micro or t3.micro
• Estimated cost: $0.00 if single instance runs continuously. Route 53 hosted zone costs $0.50/month
• Skills learned: EC2 provisioning, security groups, SSH, basic Linux administration
• Difficulty rating: 2/5

After mastering traditional compute, the natural progression moves toward serverless architecture where AWS manages all infrastructure concerns automatically.

Project 3: Build a serverless contact form with AWS Lambda and SES

Building a serverless contact form with AWS Lambda and SES demonstrates event-driven architecture patterns that dominate modern cloud applications. Lambda functions execute code in response to triggers without requiring server provisioning, patching, or capacity planning. This project combines Lambda with Simple Email Service to create a production-ready contact form backend that costs fractions of a cent per thousand submissions.

The architecture flows from an API Gateway HTTP endpoint that receives form submissions, triggers a Lambda function for validation and processing, then invokes SES to deliver the email. This pattern scales automatically from zero to thousands of concurrent requests. You pay only for actual invocations rather than idle server time.

Lambda function code and IAM roles

The Lambda function requires an execution role granting permission to write CloudWatch Logs and invoke SES SendEmail. IAM roles for Lambda follow the principle of least privilege by specifying exact actions and resource ARNs rather than wildcard permissions. The following Python function handles form processing:

import json
import boto3
from botocore.exceptions import ClientError

def lambda_handler(event, context):
    ses = boto3.client('ses', region_name='us-east-1')
    body = json.loads(event['body'])
    
    # Required CORS headers for S3 static site integration
    cors_headers = {
        'Access-Control-Allow-Origin': '*',
        'Access-Control-Allow-Headers': 'Content-Type',
        'Access-Control-Allow-Methods': 'OPTIONS,POST'
    }
    
    try:
        response = ses.send_email(
            Source='verified@yourdomain.com',
            Destination={'ToAddresses': ['owner@yourdomain.com']},
            Message={
                'Subject': {'Data': f"Contact Form: {body['subject']}"},
                'Body': {'Text': {'Data': body['message']}}
            }
        )
        return {'statusCode': 200, 'headers': cors_headers, 'body': json.dumps({'message': 'Email sent'})}
    except ClientError as e:
        return {'statusCode': 500, 'headers': cors_headers, 'body': json.dumps({'error': str(e)})}

CloudFormation starter template

Infrastructure as Code transforms manual console clicks into repeatable, version-controlled deployments. The following CloudFormation template provisions the complete serverless contact form stack including the Lambda function, API Gateway endpoint, and necessary IAM permissions:

AWSTemplateFormatVersion: '2010-09-09'
Description: Serverless Contact Form Stack

Resources:
  ContactFormFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: ContactFormHandler
      Runtime: python3.11
      Handler: index.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Code:
        ZipFile: |
          import json
          import boto3
          def lambda_handler(event, context):
              # Function code here
              return {'statusCode': 200, 'body': 'Success'}

  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: SESPermissions
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action: ses:SendEmail
                Resource: '*'

  ContactFormApi:
    Type: AWS::ApiGatewayV2::Api
    Properties:
      Name: ContactFormAPI
      ProtocolType: HTTP
      Target: !GetAtt ContactFormFunction.Arn
	  
  ApiGatewayInvokePermission:
    Type: AWS::Lambda::Permission
    Properties:
      FunctionName: !GetAtt ContactFormFunction.Arn
      Action: lambda:InvokeFunction
      Principal: apigateway.amazonaws.com
      SourceArn: !Sub "arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${ContactFormApi}/*/*"

Outputs:
  ApiEndpoint:
    Value: !Sub 'https://${ContactFormApi}.execute-api.${AWS::Region}.amazonaws.com'

• Free Tier allocation: 1 million Lambda requests, 400,000 GB-seconds compute, 3,000 SES emails monthly (for the first 12 months).
• Estimated cost: $0.00 for typical portfolio contact form traffic.
• Skills learned: Lambda functions, API Gateway, SES, IAM roles, CloudFormation
• Difficulty rating: 3/5

With serverless fundamentals established, the next project extends this pattern by adding persistent data storage through DynamoDB.

Project 4: Create a REST API with AWS API Gateway and DynamoDB

Creating a REST API with AWS API Gateway and DynamoDB builds upon serverless concepts by introducing NoSQL database design and CRUD operations. DynamoDB provides single-digit millisecond latency at any scale, making it ideal for API backends that require consistent performance. This project teaches partition key design, which directly impacts query efficiency and cost optimization.

The architecture extends the previous Lambda pattern by adding DynamoDB as a persistence layer. API Gateway routes HTTP methods to corresponding Lambda functions that perform create, read, update, and delete operations against DynamoDB tables. Understanding this pattern prepares you for building microservices architectures where each service owns its data store.

DynamoDB table design and access patterns

DynamoDB table design starts with identifying access patterns before defining the schema. Unlike relational databases where you normalize data and join tables at query time, DynamoDB requires denormalization and careful key selection to support efficient queries. For a simple items API, a partition key on itemId provides direct lookups while a Global Secondary Index on category enables filtered queries.

• Free Tier allocation: 25 GB storage, 25 WCU, 25 RCU (Provisioned capacity mode only)
• Estimated cost: $0.00 for development workloads, provided Provisioned mode is selected. On-demand mode charges per request.
• Skills learned: NoSQL design, DynamoDB operations, API versioning, IAM Least Privilege.
• Difficulty rating: 3/5

Consider the following architecture diagram that illustrates the complete REST API flow from client request through API Gateway, Lambda processing, and DynamoDB persistence.

Having built backend APIs, the next project shifts focus to frontend deployment using AWS Amplify for streamlined hosting and CI/CD integration.

Project 5: Use AWS Amplify to host a frontend app

AWS Amplify simplifies frontend deployment by providing integrated hosting, continuous deployment from Git repositories, and backend provisioning through a unified interface. Using AWS Amplify to host a frontend app teaches modern deployment workflows where pushing to a branch automatically triggers builds and deployments. This project bridges the gap between local development and production hosting.

Amplify Hosting supports frameworks including React, Vue, Angular, and static site generators like Next.js and Gatsby. The service provisions CloudFront distributions automatically, providing global edge caching without manual configuration. For beginners, Amplify abstracts complexity while still exposing underlying AWS services for customization.

Connecting Git repositories and build settings

The deployment workflow begins by connecting your GitHub, GitLab, or Bitbucket repository to Amplify. The service detects your framework and suggests build settings, though you can customize the amplify.yml file for complex build requirements. Branch-based deployments enable preview environments where pull requests deploy to unique URLs for testing before merging.

1. Navigate to AWS Amplify Console and select Host web app
2. Connect your Git provider and authorize repository access
3. Select the repository and branch for deployment
4. Review auto-detected build settings or customize amplify.yml
5. If prompted, allow Amplify to create an IAM Service Role so it can provision server-side compute resources.
6. Deploy and receive your amplifyapp.com subdomain

• Free Tier allocation: 1,000 build minutes, 15 GB served, 5 GB storage monthly (for the first 12 months)
• Estimated cost: $0.00 for personal projects. $0.01 per build minute beyond the Free Tier allowance.
• Skills learned: CI/CD pipelines, environment configuration, custom domains, IAM Service Roles for hosting.
• Difficulty rating: 2/5

With frontend hosting automated, the next project explores real-time data processing using AWS IoT Core for device connectivity and dashboards.

Project 6: Build an IoT dashboard using AWS IoT Core

Building an IoT dashboard using AWS IoT Core introduces message broker patterns and real-time data visualization. IoT Core provides secure device connectivity through MQTT protocol, enabling bidirectional communication between sensors and cloud applications. This project simulates IoT devices using Python scripts, eliminating the need for physical hardware while teaching production-ready patterns.

The architecture involves IoT Core receiving messages from simulated devices, and IoT Rules routing messages to Lambda functions or directly to DynamoDB for storage. For the frontend dashboard to display real-time updates, it must subscribe to the IoT Core broker using MQTT over WebSockets, securely authenticated via Amazon Cognito Identity Pools to grant the browser temporary access. Understanding this pub/sub messaging pattern transfers directly to other event-driven architectures using SNS, SQS, and EventBridge.

Device simulation and message routing

Simulating IoT devices requires creating thing certificates in IoT Core, downloading the certificate files, and using the AWS IoT Device SDK to publish messages. IoT Rules use SQL-like syntax to filter and transform messages before routing to downstream services. A rule selecting temperature readings above a threshold could trigger an SNS notification for alerting.

• Free Tier allocation: 250,000 messages, 250,000 rules engine actions, 50 connected devices monthly
• Estimated cost: $0.00 for development. $1.00 per million messages beyond Free Tier
• Skills learned: MQTT protocol, IoT security, message routing, real-time dashboards
• Difficulty rating: 4/5

The final project applies machine learning capabilities through AWS Rekognition, demonstrating how managed AI services integrate into applications without ML expertise.

Project 7: Image recognition using AWS Rekognition

Image recognition using AWS Rekognition showcases managed machine learning services that require no model training or ML infrastructure. Rekognition provides pre-trained models for object detection, facial analysis, text extraction, and content moderation through simple API calls. This project builds an image analysis pipeline that automatically processes uploads and stores detected labels.

The architecture triggers a Lambda function when images upload to an S3 bucket. The function calls Rekognition DetectLabels API, receives confidence-scored predictions, and stores results in DynamoDB for querying. This event-driven pattern enables batch processing of image libraries or real-time analysis of user uploads.

Rekognition API integration and response handling

The DetectLabels API accepts an S3 object reference and returns hierarchical labels with confidence percentages. Response handling should filter labels below a confidence threshold and normalize the data structure for storage. The following snippet demonstrates the integration pattern:

import boto3

def analyze_image(bucket, key):
    rekognition = boto3.client('rekognition')
    response = rekognition.detect_labels(
        Image={'S3Object': {'Bucket': bucket, 'Name': key}},
        MaxLabels=10,
        MinConfidence=80
    )
    labels = [{'name': label['Name'], 'confidence': label['Confidence']} 
              for label in response['Labels']]
    return labels

• Free Tier allocation: 1,000 images analyzed monthly for the first 12 months
• Estimated cost: $0.00 under Free Tier. $0.001 per image beyond allocation
• Skills learned: ML service integration, S3 event triggers, confidence thresholds, batch processing
• Difficulty rating: 3/5

The following comparison table summarizes all seven projects, mapping each to primary services, Free Tier viability, and skills developed.

Conclusion

These seven beginner AWS projects establish foundational cloud engineering skills through hands-on implementation rather than passive learning. Starting with S3 static hosting builds confidence with the console and CLI, while progressing through EC2, Lambda, and DynamoDB develops the architectural intuition that distinguishes capable cloud engineers. The serverless contact form project, complete with CloudFormation template, demonstrates infrastructure as code practices that scale to enterprise deployments.

Each project intentionally stays within Free Tier limits, removing cost barriers that often prevent experimentation. As you complete these projects, document your implementations in a portfolio repository with architecture diagrams and deployment instructions. This portfolio becomes tangible evidence of cloud competency during interviews, far more compelling than certification badges alone. The patterns learned here transfer directly to production systems handling millions of requests. These include event-driven architectures, least-privilege IAM, and managed service integration.

Cloud engineering evolves rapidly, but these foundational services remain stable building blocks. Master them thoroughly before chasing newer offerings. You will develop the judgment to evaluate when emerging services genuinely solve problems versus when they add unnecessary complexity.