Migrating Scriptopia Campus to AWS
Architecture Study • 2026
A cloud migration case study exploring how Scriptopia Campus can be transformed from a self-managed deployment into a scalable, highly available AWS-based platform. The proposed architecture introduces managed services, containerized workloads, centralized monitoring, automated deployments, and fault-tolerant infrastructure while maintaining compatibility with existing application services.
8
Infrastructure
5
Decisions
5
Controls
System Architecture
AWS Architecture
The proposed architecture places CloudFront at the edge for content delivery, routing traffic through an Application Load Balancer to containerized backend services. Authentication is delegated to Clerk, caching is handled through Redis, and application workloads are distributed across managed compute services. Monitoring, logging, and deployment automation are integrated to improve operational visibility and reduce maintenance overhead.
Migrating Scriptopia Campus to AWS
System
Scriptopia Campus
Architecture Study • 2026
Architecture
The proposed architecture places CloudFront at the edge for content delivery, routing traffic through an Application Load Balancer to containerized backend services. Authentication is delegated to Clerk, caching is handled through Redis, and application workloads are distributed across managed compute services. Monitoring, logging, and deployment automation are integrated to improve operational visibility and reduce maintenance overhead.
Platform Composition
Infrastructure Stack
Infrastructure
Observability
Automation
Architecture Decision Records
Engineering Decisions
Containerized Backend Services
Impact
Moving backend services to containers enables consistent deployments across environments while simplifying scaling and infrastructure management.
CloudFront for Global Delivery
Impact
Static assets and frontend resources are distributed through CloudFront to reduce latency and improve user experience across geographically distributed campuses.
Managed Redis Layer
Impact
Redis is retained as a dedicated caching layer to reduce database load and improve response times for frequently accessed placement and student data.
Serverless Background Processing
Impact
AWS Lambda is used for asynchronous operations such as notifications, report generation, imports, and scheduled processing tasks to avoid maintaining always-on compute resources.
Infrastructure as Code
Impact
Terraform enables repeatable environment provisioning and reduces configuration drift between development, staging, and production environments.
Release Architecture
Deployment Strategy
Frontend assets are deployed to S3 and distributed through CloudFront. Backend APIs are containerized and deployed to ECS. Event-driven and background workloads are executed through Lambda functions. Infrastructure provisioning is managed through Terraform and application deployments are automated through GitHub Actions.
CI/CD Pipeline
Capacity Model
Scaling Strategy
- Horizontal scaling of ECS services behind the Application Load Balancer
- Independent scaling of background processing workloads
- CloudFront edge caching for static assets
- Redis caching to reduce database pressure during placement seasons
- Multi-environment deployment strategy for development, staging, and production
Control Plane
Security
- Authentication and session management delegated to Clerk
- Private networking for internal application services
- Secrets managed through AWS Secrets Manager
- TLS encryption for all external traffic
- Role-based access control across student, recruiter, and placement officer workflows
Telemetry Surface
Monitoring & Observability
CloudWatch application and infrastructure metrics
Container health monitoring
Centralized application logging
Redis performance monitoring
API latency and error-rate dashboards
Deployment health and rollback visibility
Engineering Tradeoffs
Challenges
Challenge
The existing platform contains multiple interconnected services including assessments, recruitment workflows, student management, and analytics.
Resolution
Services are gradually containerized and migrated in phases, reducing migration risk and avoiding large-scale downtime.
Challenge
Placement drives create traffic spikes during application and assessment periods.
Resolution
Autoscaling container services combined with Redis caching absorb traffic bursts while maintaining application responsiveness.
Challenge
Operational visibility is limited in self-managed environments.
Resolution
Cloud-native monitoring, centralized logging, and alerting provide real-time insight into application health and infrastructure performance.
Challenge
Managing infrastructure manually becomes increasingly difficult as the platform grows.
Resolution
Terraform and CI/CD automation reduce manual intervention and standardize deployment workflows.
Engineering Artifacts
