AWS Real Projects Scenarios (6 Mini projects)

Important Considerations:

• Start Small: These are mini-projects! Focus on the core elements and iterate to extend functionality later.
• AWS Free Tier: Many of these projects can be started within the AWS Free Tier to manage costs. https://aws.amazon.com/free/
• Experiment and Explore: The best way to learn is by doing. Dive in and don’t be afraid to explore different combinations of services.

Project 1: Serverless Image Resizer

• Requirement: Automatically resize images for optimized website performance.
• Summary: Users upload images to S3, triggering a Lambda function to resize and store them back in S3.
• Tools/Services:
  • AWS S3
  • AWS Lambda
  • Image manipulation library (Pillow, etc., for Python-based Lambda)

Project 2: Static Website with CI/CD

• Requirement: Deploy a website, ensuring updates are reflected seamlessly.
• Summary: Create an automated pipeline to build and deploy a static website when code changes are made.
• Tools/Services:
  • AWS S3 (for website hosting)
  • AWS CodeCommit (or similar source control)
  • AWS CodeBuild (for building website resources)
  • AWS CodeDeploy (for deployment to S3)

Project 3: Scalable Web Application

• Requirement: Handle variable traffic to a web application efficiently.
• Summary: An application frontend on EC2 instances behind a load balancer, instances scale in/out based on demand.
• Tools/Services:
  • AWS EC2
  • AWS Auto Scaling Group
  • AWS Elastic Load Balancer (ELB)

Project 4: Chatbot with Natural Language Processing

• Requirement: Conversational interface for customer queries or simple tasks.
• Summary: Build a chatbot to understand basic customer requests and provide information or perform actions.
• Tools/Services:
  • AWS Lex (for building the conversational interface)
  • AWS Lambda (for backend logic and integrations)

Project 5: Serverless Data Pipeline

• Requirement: Extract, transform, and load (ETL) data for analysis or reporting.
• Summary: Use serverless technologies to collect data from a source, process it, and store it in a data warehouse.
• Tools/Services:
  • AWS S3
  • AWS Lambda
  • AWS Glue (for data transformations)
  • AWS Athena or Redshift (for data storage and querying)

Project 6: Recommendation Engine

• Requirement: Provide personalized product/content suggestions to users.
• Summary: Utilize historical data to build a recommendation system.
• Tools/Services:
  • AWS S3 (data storage)
  • AWS SageMaker (build and train recommendation models)
  • AWS Lambda (deploy the model as an API for recommendations)