AWS Grocery App Deployment

AWS & Docker Grocery Project

Real Time Installation - 10 Days

CubenSquare Customized Workshop

2-Week Live Implementation: Real-World Cloud Deployment

A 2-week, hands-on live program where you deploy a real Grocery App (v2.1) on AWS using Docker. You start with container creation and finish with a production go-live, covering CI/CD, load balancing, auto scaling, SSL, monitoring, and a managed database.

This is a project-based course. You build the full production setup yourself, step by step, with live guidance, and gain real infrastructure experience and proven workflows.

Tools & Technologies: Docker, AWS EC2, Amazon ECR, IAM, Elastic Load Balancer, CloudWatch, Linux Ubuntu 22.04, GitHub Actions, AWS CLI, Trivy

AWS Managed Services: Route 53, AWS Certificate Manager (ACM), Amazon RDS (MySQL 8.0), Amazon S3 + CloudFront

Week 1: Containerization

Days 1–7 | Docker Setup, App Packaging & Local Validation

Docker Setup & Containerization

Write a Dockerfile (base image, dependencies, runtime environment)
Build the image with docker build
Run and test the container locally
Optimize with multi-stage builds, achieving a 68% image size reduction for faster deployments
Scan images for vulnerabilities with Trivy

EC2 Instance Setup

Launch a t3.medium instance in us-east-1 running Ubuntu 22.04 LTS
Configure Security Groups with strict inbound rules (ports 80/443/22)
Attach an IAM instance role with least-privilege policies for ECR and CloudWatch
Use a User Data script to install Docker and start the service on boot

Amazon ECR Push

Create a private ECR repository
Set a lifecycle policy to retain only the last 10 images
Authenticate the Docker client using AWS CLI credentials
Tag and push the grocery app image, then verify the SHA256 digest

Week 2: Cloud Deployment

Days 8–14 | ECR Integration, EC2 Deploy & Production Observability

CI/CD Pipeline with GitHub Actions

Code commit → CI trigger → Docker build + Trivy scan → push to Amazon ECR → deploy to AWS EC2 → ELB health check → live in production
Full automation from code push to live production

Elastic Load Balancer + Auto Scaling

Distribute traffic across multiple EC2 instances running the Docker app
Route traffic only to healthy containers using ELB health checks
Configure Auto Scaling rules that adjust capacity based on CPU and request load

Monitoring with CloudWatch

Build real-time dashboards for CPU utilization, HTTP error rate, container uptime, and app latency
Set alarms for CPU and latency
Use custom metrics to reach root-cause analysis in under 3 minutes

Production-Grade Enhancements

Route 53: domain setup with latency-based routing
ACM SSL: automatic certificate provisioning and renewal for HTTPS
RDS MySQL 8.0: Multi-AZ deployment, encrypted at rest, automated snapshot backups
Amazon S3 + CloudFront: static asset delivery through an OAI-protected CDN

Final Architecture (End-to-End)
Developer → GitHub → GitHub Actions → Docker Build → Amazon ECR → EC2 Auto Scaling → ELB → Route 53 → End Users, with Amazon RDS (database) and Amazon S3 + CloudFront (static assets).

By the end of this course, you will be able to:

Deploy a production Dockerized app on AWS EC2, with resilient, scalable compute behind an Elastic Load Balancer using health checks and Auto Scaling
Build an end-to-end CI/CD pipeline with GitHub Actions that automates code build, Trivy vulnerability scanning, push to Amazon ECR, and safe deployment to EC2
Monitor and debug with CloudWatch, tracking SLOs, setting alarms for CPU and latency, and troubleshooting quickly with real-time observability
Optimize Docker images with multi-stage builds for faster ECR pushes and deployments
Secure your infrastructure with least-privilege IAM roles, strict security groups, and SSL/TLS via ACM
Design a production-ready architecture that separates S3, RDS, and EC2 so each can scale independently