AI-enabled vehicles & eSports

Implementation of a core real-time processing analytics engine to enable a platform approach

Type of data

  • WebSocket Telemetry
  • IoT data
  • Simulators data

Pathway enables an end-to-end data platform approach and integrates easily in our architecture - something we don't see with other vendors, the Python code can be maintained and versioned, and is very easy to put in production
Data Architect @Automotive Company
Data Architects
  • Unified batch and streaming engine, enabling advanced data transformations with low latency, and the ability to easily implement User-Defined Functions (UDFs) in Python at scale
  • Capacity to process millions of data points per second
  • Reconcile streaming data errors (late data, sampling frequency mismatches, multi-time series alignment)
  • Full platform approach, with a possibility of a front-end integration downstream
  • Supporting applications that involve high-frequency telemetry data streaming and advanced data transformations.
Signal Processing Engineers
  • Design User-Defined Functions for Remote Monitoring and Control: receive real-time telemetry data, including GPS location, speed, sensor readings to generate data-driven insights.
  • Fleet Management: monitoring vehicle status, routing, fuel efficiency, and maintenance needs in real time.
  • Empower Engineers: Transmit real-time data about mechanical and electrical systems, to facilitate diagnosis and prevention of mechanical failures, enabling predictive maintenance.
End Users: Drivers and eDrivers
  • Consuming dynamic dashboards for various car and performance metrics. Users receive immediate updates without the need for manual refresh.
  • Real-time alerts and status updates: ensuring drivers can adapt to changing road conditions and other variables.
  • Real-time game updates and analytics, while ensuring low latency and an immersive gaming experience.