This content originally appeared on DEV Community and was authored by Ulysses Alexandre Alves
Project Name: Cloud-Based Multi-Service Platform for Smart Event Management
Lecture about this content: https://www.youtube.com/watch?v=C4tE1kZNrX4
1. Introduction
The system is a cloud-native platform for managing large-scale events (conferences, concerts, workshops) with real-time analytics, ticketing, and personalized recommendations. It will integrate multiple services, each implemented in different languages, and leverage AI tools for development and intelligent features.
2. Overall Description
- Purpose: Provide event organizers and attendees with a seamless experience for scheduling, ticketing, and engagement.
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Scope:
- Web application for users (React + Node.js)
- Backend microservices (C#, Python, Go)
- AI-powered recommendation engine
- CI/CD pipelines for automated deployment
- Hosted on a major cloud (AWS, Azure, or GCP)
3. Functional Requirements
3.1 User Management Service (C# / .NET)
- Register, authenticate, and manage user profiles.
- Role-based access control (organizer, attendee).
3.2 Event Management Service (Go)
- Create, update, and delete events.
- Handle scheduling and capacity management.
3.3 Ticketing Service (TypeScript / Node.js)
- Purchase and validate tickets.
- Integrate payment gateway.
3.4 Recommendation Engine (Python)
- Suggest events based on user preferences and past behavior.
- Use AI/ML models for personalization.
3.5 Frontend (React)
- Responsive UI for browsing events, purchasing tickets, and viewing recommendations.
4. Non-Functional Requirements
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Performance:
- API response time < 200ms under normal load.
- Support 10,000 concurrent users.
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Reliability:
- 99.9% uptime SLA.
- Automatic failover and load balancing.
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Maintainability:
- Modular microservices architecture.
- Automated testing and linting integrated into CI/CD.
5. Technical Requirements
- Cloud: AWS (EC2, S3, RDS, Lambda) or Azure/GCP equivalents.
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CI/CD:
- Pipelines for each microservice using GitHub Actions or Azure DevOps.
- Automated build, test, and deployment.
-
AI Tools:
- Use Copilot or similar for code generation and refactoring.
- Integrate AI-assisted testing and documentation.
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Automation:
- Infrastructure as Code (Terraform or AWS CDK).
- Automated dependency updates and security scans.
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Languages:
- C# (.NET) for User Management
- Go for Event Management
- TypeScript (Node.js) for Ticketing
- Python for Recommendation Engine
- Frontend: React (TypeScript).
- Runtime: Node.js for frontend and ticketing service.
6. Architecture Overview
- Microservices deployed in containers (Docker + Kubernetes).
- API Gateway for routing requests.
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Database:
- Relational DB for user and event data (PostgreSQL).
- NoSQL for recommendation engine (MongoDB).
- Message Queue: RabbitMQ or Kafka for inter-service communication.
7. Security
- OAuth 2.0 for authentication.
- HTTPS for all endpoints.
- Data encryption at rest and in transit.
8. Testing
- Unit and integration tests for each service.
- Load testing for performance validation.
- Automated test execution in CI/CD pipeline.
Contact
If you would like to contact me to talk about this case study project or another subject, you can reach out to me through the comment sections, contact page or sending me a private message in my LinkedIn provile at https://www.linkedin.com/in/ulysses-alves/.
Thank you.
This content originally appeared on DEV Community and was authored by Ulysses Alexandre Alves
Ulysses Alexandre Alves | Sciencx (2025-11-20T20:32:49+00:00) Software Requirements Specification – Case Study 1. Retrieved from https://www.scien.cx/2025/11/20/software-requirements-specification-case-study-1-2/
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