Technology used: Docker, Airflow, DBT, PostgreSQL, Python, Power BI
A data pipeline to extract data from Fantasy Premier League (FPL) API.
The goal was to create a Power BI report for my FPL team of 2023-2024 season using the FPL API. Airflow was used to orchestrate a pipeline that would extract data for my FPL, transform and store it in a postrgeSQL database and report on it in Power BI.
- extract_FPL.py extracts data from FPL API and performs basic transformations and stores it in a tmp folder
- upload_postgeSQL.py creates database tables and loads the extracted data into PostgreSQL
- DBT models transform the raw data in PostgreSQL and generate data marts
- Data marts are used to create a FPL Power BI Report
- Airflow is used to run the python files and dbt models on a set schedule.
- Airflow and DBT are installed in docker containers
FPL Power BI Report visualizes the FPL data.
- Gameweek: Provides stats such as highest score, average score, captaincy, top scorer and chips used for the previous, current and upcoming gameweek
- Fixtures: Shows key fixtures and key players for the upcoming gameweek
- My Team: Provides stats such as current rank, total points and transfers made for my FPL team
- Team Transfers: Provides information on players transferred in and out of my FPL team and transfers made for the current gameweek and season
- Transfer Planner: Shows key fixtures and players in the remaining gameweek as well as the fixture difficulty rating provided by FPL to plan out future transfers
