Olympic Analytics Platform

🏗️ Architecture Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Sources  │    │   Data Lake     │    │   Analytics     │
│                 │    │                 │    │                 │
│ • CSV Files     │───▶│ • Raw Zone      │───▶│ • Databricks    │
│ • APIs          │    │ • Processed Zone│    │ • Synapse       │
│ • Streams       │    │ • Curated Zone  │    │ • Power BI      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Ingestion     │    │ Transformation  │    │ Visualization   │
│                 │    │                 │    │                 │
│ • Event Hubs    │    │ • PySpark       │    │ • Dashboards    │
│ • Kafka         │    │ • Data Quality  │    │ • Reports       │
│ • Batch Load    │    │ • Aggregations  │    │ • Alerts        │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🚀 Features

• Real-time Data Ingestion: Apache Kafka and Azure Event Hubs integration
• Scalable Data Processing: Azure Databricks with PySpark
• Data Lake Architecture: Multi-zone storage (Raw, Processed, Curated)
• Data Quality & Monitoring: Automated validation and alerting
• CI/CD Pipeline: Azure DevOps integration
• Interactive Dashboards: Power BI visualizations
• Configuration Management: Environment-based configuration
• Logging & Monitoring: Comprehensive observability

📁 Project Structure

olympic-analytics-platform/
├── config/                     # Configuration files
│   ├── environments/           # Environment-specific configs
│   └── templates/              # Configuration templates
├── src/                        # Source code
│   ├── ingestion/              # Data ingestion modules
│   ├── transformation/         # Data transformation logic
│   ├── quality/                # Data quality checks
│   ├── utils/                  # Utility functions
│   └── tests/                  # Unit tests
├── notebooks/                  # Databricks notebooks
├── pipelines/                  # Azure DevOps pipelines
├── terraform/                  # Infrastructure as Code
├── docs/                       # Documentation
└── data/                       # Sample data and schemas

🛠️ Technology Stack

• Cloud Platform: Microsoft Azure
• Data Ingestion: Azure Event Hubs, Apache Kafka
• Data Processing: Azure Databricks, PySpark
• Data Storage: Azure Data Lake Storage Gen2
• Data Warehouse: Azure Synapse Analytics
• Orchestration: Azure Data Factory
• Visualization: Power BI
• CI/CD: Azure DevOps
• Infrastructure: Terraform
• Monitoring: Azure Monitor, Application Insights

🚀 Quick Start

Prerequisites

• Azure Subscription
• Python 3.8+
• Azure CLI
• Terraform
• Docker (optional)

Installation

1. Clone the repository

   git clone <repository-url>
   cd olympic-analytics-platform

2. Set up environment

   # Create virtual environment
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   
   # Install dependencies
   pip install -r requirements.txt

3. Configure Azure resources

   # Login to Azure
   az login
   
   # Deploy infrastructure
   cd terraform
   terraform init
   terraform plan
   terraform apply

4. Set up configuration

   # Copy configuration template
   cp config/templates/config.template.yaml config/environments/dev.yaml
   
   # Update with your Azure resource details
   # Update connection strings, endpoints, etc.

5. Run the pipeline

   # Start data ingestion
   python src/ingestion/main.py
   
   # Run transformations
   python src/transformation/main.py

📊 Data Models

Core Entities

• Athletes: Olympic athletes with performance data
• Teams: National teams and their compositions
• Medals: Medal counts and rankings by country
• Events: Olympic events and disciplines
• Coaches: Team coaches and their associations

Data Quality Rules

• No null values in required fields
• Valid country codes
• Consistent date formats
• Medal counts validation
• Gender distribution validation

🔧 Configuration

The platform uses environment-based configuration:

# config/environments/dev.yaml
azure:
  subscription_id: "your-subscription-id"
  resource_group: "olympic-analytics-rg"
  
databricks:
  workspace_url: "https://your-workspace.azuredatabricks.net"
  cluster_id: "your-cluster-id"
  
storage:
  account_name: "yourstorageaccount"
  container_raw: "raw-data"
  container_processed: "processed-data"
  
event_hubs:
  namespace: "olympic-events"
  hub_name: "olympics-data"

📈 Monitoring & Alerting

• Data Quality Metrics: Automated validation results
• Pipeline Performance: Processing times and throughput
• Error Tracking: Failed records and exceptions
• Resource Utilization: CPU, memory, and storage usage

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

For support and questions:

• Create an issue in the repository
• Check the documentation
• Review the troubleshooting guide

🔄 Version History

• v1.0.0: Initial production release
• v0.9.0: Beta release with core functionality
• v0.8.0: Alpha release with basic pipeline