A simple dashboard to view driver race position based on the race venue and also shows track temperature and humidity. Currently shows only historical data for year 2024.
This project demonstrates an ETL workflow using Python and Streamlit, powered by the Open F1 API.
Extract: Pull race data from the API (leaders, track temperature, humidity).
Transform: Clean, sort, and structure the data for each race track.
Load/Visualize: Display insights in an interactive Streamlit dashboard where users can select a track and view the leader and race-day conditions.
Although currently implemented in a batch mode, this project will be expanded to include real-time data ingestion and streaming ETL pipelines (Kafka/Spark).
Plan: F1 API --> Azure Functions --> Event Hubs --> Databricks (scheduled Jobs) --> Delta Lake --> Dashboard
Python (data extraction + cleaning)
Databricks(Realtime data Ingestion + storage)
Streamlit (interactive dashboard)
Open F1 API (data source)
Dropdown selection of track (2024 races).
Display of race leader, track temperature, and humidity.
Simple data cleaning pipeline before visualization.
Implement scheduling for automated daily ingestion.
Build a real-time ETL version using Kafka/Spark/Flink.
Add database support (Postgres/DuckDB).
Enhance dashboard with live updating leaderboards and team stats.
git clone https://github.com/vivupadi/Formula_1.git
pip install -r requirements.txt
streamlit run Formula_1.py
This project is licensed under the MIT License - see the LICENSE file for details.
Made with ❤️ by Vivek Padayattil