A comprehensive data engineering project that processes, transforms, and analyzes Olympic data using advanced ETL pipelines, data warehousing techniques, and machine learning models to predict medal outcomes and analyze athlete performance patterns.
This data engineering project demonstrates end-to-end data pipeline development, from raw data ingestion to actionable insights. The solution processes multi-source Olympic datasets, implements robust data validation, and builds scalable machine learning models for sports analytics and performance prediction.
- Data Ingestion - Multi-source data collection and validation
- Data Transformation - Complex data cleaning and feature engineering
- Data Loading - Optimized data storage and indexing
- Data Quality - Automated validation and monitoring
- Data Orchestration - Workflow management and scheduling
- Apache Spark - Distributed data processing and ETL operations
- Pandas - Data manipulation and analysis
- SQLAlchemy - Database operations and ORM
- Apache Airflow - Workflow orchestration and scheduling
- Docker - Containerized data processing environments
- Python 3.9+ - Core programming language
- Apache Spark 3.4+ - Distributed data processing
- Pandas 2.1+ - Data manipulation and analysis
- SQLAlchemy - Database operations and ORM
- Apache Airflow - Workflow orchestration
- Docker - Containerization and deployment
- PostgreSQL - Data warehousing and storage
- Scikit-learn - Machine learning algorithms
- NumPy - Numerical computing
- Matplotlib/Seaborn - Data visualization
- Jupyter Notebooks - Interactive data analysis
- MLflow - Model tracking and management
- Data Validation - Great Expectations for data quality
- Data Lineage - Apache Atlas for data governance
- Monitoring - Prometheus and Grafana for pipeline monitoring
- Storage - S3-compatible object storage for data lakes
OlympicDataAnalysis/
├── data/ # Data management
│ ├── raw/ # Raw data ingestion
│ │ ├── athletes.csv
│ │ ├── teams.csv
│ │ ├── medals.csv
│ │ ├── coaches.csv
│ │ └── entries_gender.csv
│ ├── processed/ # Cleaned and transformed data
│ │ ├── bronze/
│ │ ├── silver/
│ │ └── gold/
│ └── curated/ # Business-ready datasets
├── pipelines/ # ETL pipeline code
│ ├── ingestion/ # Data ingestion pipelines
│ │ ├── raw_data_ingestion.py
│ │ └── data_validation.py
│ ├── transformation/ # Data transformation logic
│ │ ├── data_cleaning.py
│ │ ├── feature_engineering.py
│ │ └── data_quality_checks.py
│ ├── loading/ # Data loading operations
│ │ ├── data_warehouse_loader.py
│ │ └── data_lake_loader.py
│ └── orchestration/ # Workflow orchestration
│ ├── dags/ # Apache Airflow DAGs
│ └── workflows/ # Pipeline workflows
├── models/ # Machine learning models
│ ├── training/ # Model training pipelines
│ ├── inference/ # Model inference services
│ └── monitoring/ # Model performance monitoring
├── infrastructure/ # Infrastructure as Code
│ ├── docker/ # Docker configurations
│ ├── kubernetes/ # K8s deployment configs
│ └── terraform/ # Cloud infrastructure
├── monitoring/ # Data pipeline monitoring
│ ├── dashboards/ # Grafana dashboards
│ ├── alerts/ # Alert configurations
│ └── metrics/ # Custom metrics
├── notebooks/ # Jupyter notebooks
│ ├── 01_data_exploration.ipynb
│ ├── 02_data_quality_analysis.ipynb
│ ├── 03_feature_engineering.ipynb
│ ├── 04_model_development.ipynb
│ └── 05_pipeline_monitoring.ipynb
├── tests/ # Test suites
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ └── data_quality/ # Data quality tests
├── docs/ # Documentation
│ ├── api/ # API documentation
│ ├── architecture/ # System architecture docs
│ └── runbooks/ # Operational runbooks
├── requirements.txt # Python dependencies
├── docker-compose.yml # Local development setup
├── Dockerfile # Container configuration
└── README.md # Project documentation
# Multi-source data ingestion
- Raw data collection from various sources
- Data format validation and schema enforcement
- Incremental data loading and change data capture
- Data quality checks and anomaly detection
- Metadata extraction and cataloging# Distributed data processing
- Apache Spark for large-scale data transformation
- Data cleaning and standardization
- Feature engineering and aggregation
- Data partitioning and optimization
- Parallel processing and performance tuning# Multi-tier data storage
- Raw data lake for historical data
- Processed data warehouse for analytics
- Feature store for ML features
- Data catalog for metadata management
- Backup and disaster recovery# Data quality management
- Automated data validation rules
- Data lineage tracking and documentation
- Data quality monitoring and alerting
- Compliance and privacy controls
- Data retention and archival policies- Multi-source Support - CSV, JSON, Parquet, and database sources
- Schema Evolution - Handling changing data schemas over time
- Data Validation - Automated validation of data quality and completeness
- Error Handling - Robust error handling and retry mechanisms
- Monitoring - Real-time monitoring of ingestion processes
- Data Cleaning - Automated cleaning of missing values and outliers
- Feature Engineering - Creation of derived features and aggregations
- Data Standardization - Consistent formatting and data types
- Performance Optimization - Optimized transformations for large datasets
- Testing - Comprehensive testing of transformation logic
- Incremental Loading - Efficient loading of new and changed data
- Data Partitioning - Optimized data partitioning for query performance
- Indexing - Strategic indexing for fast data retrieval
- Compression - Data compression for storage optimization
- Backup - Automated backup and recovery procedures
- Feature Engineering - Automated feature creation and selection
- Model Training - Distributed model training on large datasets
- Hyperparameter Tuning - Automated hyperparameter optimization
- Model Evaluation - Comprehensive model performance assessment
- Model Versioning - Version control for ML models
- Model Serving - Real-time model inference services
- A/B Testing - Framework for testing different model versions
- Model Monitoring - Continuous monitoring of model performance
- Retraining - Automated model retraining pipelines
- Rollback - Safe model rollback capabilities
- Interactive Dashboards - Real-time analytics dashboards
- Ad-hoc Analysis - Flexible data exploration capabilities
- Report Generation - Automated report generation and distribution
- Data Storytelling - Narrative-driven data insights
- Performance Metrics - Key performance indicators and metrics
- Medal Distribution Analysis - Country-wise medal performance
- Gender Participation Trends - Gender equality in sports
- Athlete Performance Metrics - Individual and team performance
- Historical Trends - Long-term performance patterns
- Predictive Analytics - Future performance predictions
- Python 3.9+
- Apache Spark 3.4+
- Docker and Docker Compose
- PostgreSQL 13+
- Apache Airflow 2.5+
- Clone the repository:
git clone https://github.com/yourusername/OlympicDataAnalysis.git
cd OlympicDataAnalysis- 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- Start infrastructure:
# Start local infrastructure with Docker
docker-compose up -d
# Initialize Airflow
airflow db init
airflow users create --username admin --password admin --firstname Admin --lastname User --role Admin --email [email protected]- Run data pipeline:
# Start Airflow webserver
airflow webserver --port 8080
# Start Airflow scheduler
airflow scheduler- Run ETL Pipeline:
# Execute data ingestion
python pipelines/ingestion/raw_data_ingestion.py
# Run data transformation
python pipelines/transformation/data_cleaning.py
# Load data to warehouse
python pipelines/loading/data_warehouse_loader.py- Monitor Pipeline:
# Access Airflow UI at http://localhost:8080
# Monitor pipeline execution and logs- Run Analytics:
# Execute data analysis
python notebooks/01_data_exploration.ipynb
# Generate visualizations
python notebooks/02_data_visualization.ipynb- Train Models:
python models/training/medal_predictor.py- Make Predictions:
python models/inference/predict_medals.py- Monitor Performance:
python models/monitoring/model_monitor.py- Completeness - Percentage of non-null values
- Accuracy - Data accuracy against business rules
- Consistency - Data consistency across sources
- Timeliness - Data freshness and update frequency
- Validity - Data format and range validation
- Pipeline Health - Real-time pipeline status monitoring
- Data Quality Trends - Historical data quality metrics
- Performance Metrics - Processing time and throughput
- Error Tracking - Error rates and failure analysis
- Resource Utilization - CPU, memory, and storage usage
- Partitioning Strategy - Optimized data partitioning for query performance
- Caching - Intelligent caching of frequently accessed data
- Compression - Data compression for storage efficiency
- Parallel Processing - Multi-threaded and distributed processing
- Resource Tuning - Optimized resource allocation and configuration
- Indexing - Strategic database indexing
- Query Rewriting - Optimized SQL query generation
- Materialized Views - Pre-computed aggregations
- Connection Pooling - Efficient database connection management
- Query Caching - Caching of frequently executed queries
# Deploy with Kubernetes
kubectl apply -f infrastructure/kubernetes/
# Deploy with Docker Swarm
docker stack deploy -c docker-compose.prod.yml olympic-analysis# Deploy to AWS
terraform apply -f infrastructure/terraform/aws/
# Deploy to Azure
terraform apply -f infrastructure/terraform/azure/- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Data Source: Olympic Data
- Apache Spark - Distributed data processing
- Apache Airflow - Workflow orchestration
- Docker - Containerization platform
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