As a lifelong Ferrari and Formula 1 enthusiast, this project is more than just a technical case study—it’s a tribute to a childhood passion.

In 2023, I had the chance to visit Maranello, the heart of the Prancing Horse. Walking through the Ferrari Museum, seeing the legendary F1 cars up close, and breathing in decades of racing history lit a spark.

This project explores the historical performance of Scuderia Ferrari in Formula 1 from 1950 to today, using real-world data to deliver insightful visualizations and interactive reporting tools.

Through this case study, I aimed to:

Understand long-term performance trends of Ferrari in F1.
Automate data ingestion, transformation, and reporting.
Build a reproducible and maintainable data pipeline.
Practice full-stack data engineering with SQLite, Python, and Streamlit.

Tools & Technologies Used

Data source: Ergast API (historical F1 race data)
Database: SQLite
Data processing: Python (requests, pandas, sqlite3)
Visualization / Dashboard: Streamlit, Matplotlib, Plotly
Version control: Git + GitHub

Database Architecture

I designed a normalized relational database in SQLite to structure the racing data, using the following main tables:

drivers: driver information
constructors: constructor information
races: Grand Prix calendar
circuits: circuit names and locations
results: race results (position, driver, constructor, points, status)
(Planned extensions: lap_times, pit_stops, qualifying)

Example of a SQL query :

SELECT year, COUNT(*) AS wins
FROM results
JOIN races ON results.raceId = races.raceId
WHERE constructorId = (SELECT constructorId FROM constructors WHERE name = 'Ferrari') 
  AND position = 1
GROUP BY year;