Powering Real-Time Vehicle Intelligence at Rivian with Apache Flink

At Rivian, our mission is to design and build vehicles that inspire and enable sustainable exploration while delivering a seamless, intelligent user experience. Our connected fleet streams real-time telemetry including sensor data that includes location, battery SOC, etc. To turn this firehose of raw information into instant driver alerts and years of searchable insight, we rely on Apache Flink, Kafka.

In this talk, we’ll show how we built a scalable, cloud-native stack that powers real-time features and long-term intelligence.

Our vehicles generate a continuous stream of telemetry data. To handle this firehose of information, we’ve built a robust stream processing architecture centered around Flink ingestion pipelines. These pipelines process and enrich the data in real time, powering both internal analytics and external customer experiences.

One of the standout components of our platform is Event Watch, which is a Flink-powered feature that allows teams and customers to define streaming jobs that detect key events like abnormal battery drain, collision detection or vehicle movement in/out off a geofence. These events trigger mobile push notifications instantly, enabling proactive maintenance, safety features, and personalized alerts.

Beyond real-time event detection, we’ve designed our system for both low-latency responsiveness and long-term analytical depth. Processed telemetry is stored in Databricks Delta tables for scalable historical analysis, while a time series database supports fast, live queries for dashboards and monitoring systems.

We’ll walk through how we’ve architected this dual-purpose system; balancing high-throughput stream processing with the flexibility to drill down into historical trends. We’ll also cover how Flink’s stateful processing model enables complex event patterns and reliable delivery, even at scale.

Join us to learn how Rivian is building the future of connected vehicles one event stream at a time.