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BMW Cloud Data Hub: A reference implementation of the modern data architecture on AWS
How BMW Group built a modern data mesh on AWS with advanced dataset discovery, automated data quality checks, and self-service analytics. The Cloud Data Hub enables teams across the enterprise to find trusted datasets via intelligent search and query data to generate new insights.
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Developing a Platform for Software-defined Vehicles with Continental Automotive Edge (CAEdge)
How Continental built CAEdge — a modular middleware framework connecting vehicle ECUs to the cloud. Enables OTA updates, remote diagnostics, and CI/CD pipelines for automotive software, bringing cloud-native development practices to software-defined vehicles.
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Develop and deploy a customized workflow using Autonomous Driving Data Framework (ADDF) on AWS
Build and deploy custom autonomous driving workflows with ADDF — a fully automated reference solution offering pre-built data pipelines, visualization dashboards, annotation interfaces, and extraction classifiers. ADDF reduces deployment time and ensures AD workloads follow AWS Well-Architected best practices out of the box.
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How to Run Massively Scalable ADAS Simulation Workloads on CAEdge
Orchestrate massively parallel ADAS simulation workloads using CAEdge with Amazon MWAA and Amazon EKS. The CAEdge Framework delegates high-performance computing jobs to EKS clusters, enabling automotive teams to run thousands of concurrent simulation scenarios while leveraging specialization and parallel processing at scale.
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Field Notes: Deploy and Visualize ROS Bag Data on AWS using rviz and Webviz for Autonomous Driving
Set up cloud-based 3D visualization of autonomous driving sensor data using two complementary tools — rviz for point cloud rendering and Webviz for browser-based replay of lidar, camera, and radar recordings. Enables remote teams to inspect and debug driving recordings without local infrastructure.