How to Build a Casino Game Tracker: Web Scraping 500 Online Casinos

Simple Summary

This project aims to create a powerful web scraping application that extracts game information from 500 online casino homepages, providing valuable insights into game popularity and placement trends across the industry.

Product Requirements Document (PRD)

Goals:

1. Create a web scraping application capable of extracting game names and their order of appearance from 500 online casino homepages.
2. Develop a system to store and manage the scraped data efficiently.
3. Implement a scheduling system for regular data updates.
4. Create a user interface to display and analyze the collected data.

Target Audience:

• Online casino operators
• Game developers
• Market researchers in the online gambling industry

Key Features:

1. Web scraping engine capable of handling 500 websites
2. Data storage and management system
3. Scheduling system for automated scraping
4. Data visualization dashboard
5. Search and filter functionality for analyzed data
6. Export capabilities for reports and raw data

User Requirements:

1. Ability to view a list of games and their prominence across multiple casinos
2. Option to filter data by casino, game, or date range
3. Visualizations showing trends in game placement over time
4. Ability to export data for further analysis
5. User-friendly interface for navigating and interpreting the data

User Flows

1. Data Collection Flow:

   • System initiates scraping process for 500 casino websites
   • Data is extracted, processed, and stored in the database
   • User receives notification of completed scraping cycle

2. Data Analysis Flow:

   • User logs into the dashboard
   • User selects date range and specific casinos or games to analyze
   • System generates visualizations and reports based on selected criteria
   • User explores data through interactive charts and tables

3. Export Flow:

   • User selects desired data set for export
   • User chooses export format (CSV, JSON, etc.)
   • System generates and provides download link for exported data

Technical Specifications

• Backend: Python with FastAPI for API development
• Web Scraping: Scrapy or Beautiful Soup
• Database: PostgreSQL for structured data storage
• Frontend: React.js for building the user interface
• Data Visualization: D3.js or Chart.js for creating interactive charts
• Task Scheduling: Celery for managing periodic scraping tasks
• Containerization: Docker for easy deployment and scaling
• Cloud Platform: AWS or Google Cloud for hosting

API Endpoints

1. GET /api/games - Retrieve list of games across all casinos
2. GET /api/casinos - Retrieve list of all tracked casinos
3. GET /api/trends - Get trend data for game placements
4. POST /api/scrape - Manually trigger a scraping cycle
5. GET /api/export - Generate and retrieve export file

Database Schema

1. Casinos Table:

   • id (Primary Key)
   • name
   • url
   • last_scraped_at

2. Games Table:

   • id (Primary Key)
   • name

3. GamePlacements Table:

   • id (Primary Key)
   • casino_id (Foreign Key to Casinos)
   • game_id (Foreign Key to Games)
   • position
   • scraped_at

File Structure

Implementation Plan

1. Set up project structure and version control
2. Develop basic scraping functionality for a single casino
3. Implement database schema and data storage
4. Scale scraping to handle 500 casinos
5. Develop API endpoints for data retrieval
6. Create frontend dashboard with basic visualizations
7. Implement user authentication and authorization
8. Develop advanced filtering and search capabilities
9. Create data export functionality
10. Implement automated scheduling for regular scraping
11. Optimize performance and error handling
12. Conduct thorough testing and bug fixing
13. Deploy to production environment

Deployment Strategy

1. Containerize the application using Docker
2. Set up CI/CD pipeline using GitHub Actions or GitLab CI
3. Deploy backend to cloud platform (e.g., AWS ECS or Google Cloud Run)
4. Deploy frontend to CDN (e.g., AWS CloudFront or Google Cloud CDN)
5. Set up database in cloud (e.g., AWS RDS or Google Cloud SQL)
6. Configure load balancing and auto-scaling for the backend
7. Implement monitoring and logging (e.g., Prometheus, Grafana)
8. Conduct security audit and penetration testing
9. Perform gradual rollout and monitor for issues
10. Establish backup and disaster recovery procedures

Design Rationale

The chosen architecture separates concerns between backend (data collection and processing) and frontend (data visualization and user interaction). Python is selected for its strong web scraping libraries and data processing capabilities. A relational database (PostgreSQL) is used due to the structured nature of the data and the need for complex queries. The frontend uses React for its component-based architecture and excellent performance for data-heavy applications. Docker is employed to ensure consistency across development and production environments, while cloud deployment allows for scalability to handle the large number of websites being scraped.