De voltas rápidas a decisões rápidas: uma arquitetura orientada a eventos para telemetria dos carros de Fórmula 1

Abstract

Formula 1 is a highly technological environment characterized by the continuous generation of large volumes of real-time telemetry data, which are fundamental for strategic and operational decision-making. In this context, the ability to collect, process, and make this information available with low latency becomes a key competitive advantage. This study proposes the development of a data architecture based on the event-driven paradigm, aiming to enable the simultaneous processing of real-time and batch data. The solution was designed to simulate the complete telemetry data flow of a Formula 1 car, using the F1 23 simulator as the source. The implemented architecture comprises a data ingestion module using the UDP protocol—responsible for capturing and pre-processing the information—followed by a messaging system that ensures scalable and fault-tolerant event transmission. From this layer, data is routed along two paths: a "fast layer" responsible for real-time processing and visualization, and a "batch layer" responsible for storage in a data lake and subsequent processing in a relational data warehouse. The adopted methodology is based on the construction and experimental validation of the pipeline using exclusively open-source technologies, focusing on performance, scalability, and component decoupling. The results demonstrate the viability of the proposed architecture for handling high-velocity and high-volume data, while also highlighting its potential for applications in real-world data engineering scenarios. It is concluded that an event-driven architecture is effective for processing real-time telemetry data, enabling not only instant analysis during the race but also structured storage for subsequent analysis and future applications, such as machine learning models.