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How Tesla is using Kubernetes and Kafka to handle trillions of events per day
How Cloud-Native technologies and Event-Driven systems enable critical automotive innovations at scale
In the fast-evolving automotive industry, leveraging real-time data from sensors is transforming not only self-driving but also enabling deeper, real-time insights for the drivers into vehicle performance while allowing engineering teams to preemptively fix issues before they are even noticed.
In this article, we show how Tesla is using Cloud-Native technologies along with Event-Driven systems to unlock insights into their millions of cars by processing trillions of events per day, with just a minimal engineering team.
Disclaimers: The views expressed in this article are my own and do not reflect those of any past, present, or future employer. Unless stated otherwise, all diagrams and figures in this article are made by the author.
The Data Deluge in modern vehicles
Nowadays, cars generate data about how they are used, where they are, and who’s behind the wheel. The name of the game here is how to turn these data into valuable products and services?
As the car continues its transition from a hardware-driven machine to a software-driven electronics device, Tesla’s vehicles are equipped with tens of sensors that continuously generate data in the form of events.
These events, related to everything from engine performance to driving conditions as well as car surroundings, must be processed and made available to downstream applications/systems or stored in databases for future use.
The architecture to manage such a vast amount of data consists of two main components:
- Data Generation Devices: These include the sensors and onboard computers in each Tesla vehicle.
- Server-Side Data Infrastructure: This handles the entire data pipeline, from collecting events to processing, storing, and serving the data.
In this article, we focus on the Server-Side part to understand how Tesla deals with the challenge of supporting…
