# Terraform — an S3 bucket for course content
# plus a CloudFront CDN for global delivery
# (a very common edtech setup)

provider "aws" {
  region = "ap-south-1"
}

resource "aws_s3_bucket" "course_content" {
  bucket = "edtech-course-content"
}

resource "aws_s3_bucket_versioning" "v" {
  bucket = aws_s3_bucket.course_content.id
  versioning_configuration {
    status = "Enabled"
  }
}

resource "aws_cloudfront_distribution" "cdn" {
  enabled = true

  origin {
    domain_name = aws_s3_bucket.course_content.bucket_domain_name
    origin_id   = "s3-content"
  }

  default_cache_behavior {
    target_origin_id       = "s3-content"
    viewer_protocol_policy = "redirect-to-https"
    allowed_methods        = ["GET", "HEAD"]
    cached_methods         = ["GET", "HEAD"]

    forwarded_values {
      query_string = false
      cookies { forward = "none" }
    }
  }

  restrictions {
    geo_restriction {
      restriction_type = "none"
    }
  }

  viewer_certificate {
    cloudfront_default_certificate = true
  }
}

# Kubernetes — an auto-scaled exam-service deployment
# The real edtech problem: thousands of students at 10:00 AM sharp

apiVersion: apps/v1
kind: Deployment
metadata:
  name: exam-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: exam-service
  template:
    metadata:
      labels:
        app: exam-service
    spec:
      containers:
        - name: app
          image: myregistry/exam-service:1.0.0
          ports:
            - containerPort: 8080
          resources:
            requests:
              cpu: "200m"
              memory: "256Mi"
            limits:
              cpu: "1000m"
              memory: "512Mi"

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: exam-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: exam-service
  minReplicas: 3
  maxReplicas: 50
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 60

# GitHub Actions — build, test, and deploy on every push
# Edtech teams push constantly; reliable CI/CD matters

name: Deploy

on:
  push:
    branches: [main]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Build image
        run: |
          docker build -t exam-service:${{ github.sha }} .

      - name: Run tests
        run: docker run --rm exam-service:${{ github.sha }} \
             pytest -q

      - name: Push to registry
        env:
          REGISTRY_TOKEN: ${{ secrets.REGISTRY_TOKEN }}
        run: |
          echo "$REGISTRY_TOKEN" | docker login myregistry.io \
            -u ${{ secrets.REGISTRY_USER }} --password-stdin
          docker tag exam-service:${{ github.sha }} \
            myregistry.io/exam-service:${{ github.sha }}
          docker push myregistry.io/exam-service:${{ github.sha }}

      - name: Deploy
        run: |
          # e.g., kubectl set image or Helm upgrade
          echo "Deploying commit ${{ github.sha }}"

Project 1 — Course content delivery on AWS
1. S3 bucket for videos and PDFs
2. CloudFront CDN for global delivery
3. Signed URLs for paid content
4. Basic monitoring with CloudWatch

Why it works:
- Directly mirrors real edtech architecture
- Shows S3, CDN, IAM, and cost awareness
- Easy to demo with a public link

Project 2 — Auto-scaled quiz service
1. Dockerise a simple quiz API
2. Deploy on ECS or Kubernetes
3. Add a load test (k6 or Locust)
4. Configure auto-scaling under load
5. Screenshot before/after scaling

Why it works:
- Solves the classic "exam-day spike" problem
- Shows Docker, Kubernetes, and autoscaling
- Strong story for interviews

Weekly study plan (10-12 hrs):
Mon 1.5h — Theory (AWS or Linux)
Tue 1.5h — Hands-on labs
Wed 1.5h — Theory (K8s or Terraform)
Thu 1.5h — Hands-on labs
Fri 1h   — Weekly review
Sat 3h   — Project work
Sun 1.5h — Practice exams / rest

Consistency beats intensity.

Job search plan (months 9-12):
Week 1: Resume + LinkedIn (edtech focus)
Week 2: GitHub cleanup + READMEs
Week 3: 10 applications / week
Week 4: 2 mock interviews
Week 5: Follow up + iterate
Week 6: Network on LinkedIn

Target companies first, then scale out.
Reference the edtech use cases in your cover note.