This repository contains data engineering code and analysis of historical ATP tennis world tour data from 2000 to 2022. The project explores various aspects of the data, such as player performance, match outcomes, and tournament trends. The code includes data cleaning, transformation, and visualization techniques. The project aims to provide insights into the world of professional tennis and develop data-driven models for predicting match outcomes.
- Airflow: A workflow management platform used to orchestrate and automate data pipelines.
- Apache Beam: A unified programming model for batch and streaming data processing.
- PostgreSQL: An open-source relational database management system used to store and manage data.
- Google Cloud Storage (GCS): A scalable and durable object storage service for storing and accessing data.
- BigQuery: A fully managed, serverless data warehouse for large-scale data analytics.
- Pub/Sub: A fully managed real-time messaging service for communication between applications.
- Data Build Tool (DBT): A data transformation tool used to create and manage data transformations in SQL.
To ensure consistent and reproducible development and deployment environments, this project utilizes Docker containers. Dockerfiles are provided for building custom images for each component of the data pipeline. These images encapsulate the necessary dependencies and configurations for running the code in a consistent and isolated manner. This approach facilitates portability and reproducibility across different development and production environments.
The data used in this project is obtained from the ATP Tennis Rankings, Results, and Stats repository on GitHub https://github.com/JeffSackmann/tennis_atp. This repository contains a comprehensive collection of ATP match results, player rankings, and biographical information. The data is provided in CSV format and spans from 1968 to the present. However, for this project, the focus is on a 20-year period between 2000 and 2019, using men's singles data on the ATP tour only (excluding doubles and ATP futures/challenger series).
Professional tennis is a highly competitive and data-driven sport. Understanding player performance, match outcomes, and tournament trends can provide valuable insights for coaches, players, and fans. Data engineering plays a crucial role in collecting, processing, and analyzing large amounts of tennis data. This capstone project aims to demonstrate the application of data engineering techniques to ATP tennis data, providing insights and potential business opportunities.
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Player development: Identifying key performance indicators and patterns that contribute to player success can inform training strategies and optimize player development programs.
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Match prediction: Developing data-driven models for predicting match outcomes can provide valuable information for betting, sports journalism, and fan engagement.
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Tournament analysis: Analyzing tournament data can help identify factors that influence tournament attendance, sponsorship revenue, and overall success.
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Fan engagement: Creating data-driven visualizations and interactive dashboards can enhance fan engagement and provide personalized recommendations for matches and players.
This capstone project demonstrates the potential of data engineering to transform the way tennis data is analyzed and utilized, leading to valuable insights and business opportunities.
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Architecture: The high level project architecture can be found here: architecture.md.
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Airflow: For detailed instructions on setting up and using Airflow, please refer to the official documentation: https://airflow.apache.org/docs/. Airflow DAGs architecture can be found here: airflow.md.
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DBT: For comprehensive documentation on DBT installation, configuration, and usage, please visit the official documentation: https://docs.getdbt.com/docs/collaborate/documentation. The data model architecture for DBT can be found here: dbt.md.
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Google Cloud: To set up and utilize Google Cloud services, including GCS, BigQuery, and Pub/Sub, follow the official documentation and tutorials provided by Google: https://cloud.google.com/docs.
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Docker: For comprehensive instructions on installing and using Docker, please refer to the official documentation: https://docs.docker.com/.
The project can be further enhanced by incorporating additional technologies and strategies:
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Apache Kafka: Implementing Kafka as a stream processing platform can provide real-time data ingestion and processing capabilities, enabling the project to handle live match data and generate real-time insights.
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Fully managed compute services: Utilizing fully managed compute services on the cloud, such as Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS), can streamline the deployment and management of data processing pipelines, ensuring scalability and reliability.
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Terraform: Employing Terraform as an infrastructure as code (IaC) tool can automate the provisioning and management of cloud resources, ensuring consistency and repeatability in infrastructure deployment.
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Machine learning: Integrating machine learning techniques can enhance the project's capabilities, enabling the development of more sophisticated prediction models and deeper insights into player performance and match outcomes.
By incorporating these additional technologies and strategies, the project can evolve into a more comprehensive and powerful data engineering solution for analyzing and utilizing ATP tennis data.