Creating a Data Platform for Software-Defined Electric Vehicles
summary
Rivian’s data platform ingests telemetry from 65–70k vehicles, decodes packet captures in Go, and publishes Parquet/Delta on S3 to a Lakehouse holding 12 petabytes while a streaming tier processes a trillion data points per day. The stack uses Kafka, Flink, Spark, Databricks, DBT for real-time anomaly detection, fleet analytics, and model inference; APIs feed diagnostics and mobile apps and OTA updates push fixes — charging performance reportedly improves by 133%.
AI-written summary · about
chapters (9)
- 0:00Rivian’s data-driven EV platform
- 2:29Software-defined vehicles
- 7:56Cloud-native data platform challenges
- 11:26Data platform architecture
- 13:53Lakehouse with Delta Lake
- 16:22Vehicle telemetry use cases
- 18:47Data improves driving
- 19:50Takeaways and future
- 20:34Q&A on connectivity
related talks
30mData Platform Infrastructure at Rivian: A Deep Dive
28mTransforming Automotive Infotainment with Data
26mData Driven Culture in Software Defined Vehicles
24mRivian: Scaling Data & ML Platforms
45mAccelerated analytics: Building the next-gen data platform for Hertz
27mData-driven and cloud-native ecosystems in the automotive industry
34mOverview of the Databricks Platform
19mLessons from Building a Large-scale, Multi-cloud Data Platform at Databricks