Self-Driving Cars—Driving Hyperscale in the Data Center

Published on Jul 10, 2017 in Quantum

This post was originally posted to the Quantum website.

A s companies accelerate their development efforts for self-driving cars and advanced driver assistance systems (ADAS), they are expanding the size of their vehicle test fleets. And with each vehicle generating 2 PB or more of data annually, these companies need a good data management plan to support this large and rapidly growing volume of data.

The following table highlights the magnitude of this challenge, based on a nominal 9 TB of data being generated daily, per car.

As you can see, a relatively small test fleet of five cars would generate about 1 PB of data monthly. This volume of data requires a storage infrastructure that meets performance requirements at scale, and can easily and cost-effectively scale capacity. It’s necessary to retain this data for long periods of time to feed artificial intelligence (AI) engines and backtest new software algorithms, and also for regulatory purposes.

Without proper storage planning, development efforts can suffer due to storage costs consuming too much of a project’s budget, and storage performance slowing down as the volume of data under management exceeds system capabilities.

Quantum understands these challenges. Our automotive solutions are proven to deliver high performance at scale with easy—massive—scalability and integrated data protection to provide complete data management solutions at one-tenth the cost of alternatives.

Click here to read the rest of this post.