Efficient On-Premises Data Storage Solution for Autonomous Driving

Numerous improvements of the algorithms are necessary to perfect the control software for autonomous driving. To train and test algorithm we need sensor data. One of the major challenges are to deal with deluge of digital content and unstructured data with growing importance of data protection. The recent trend in automotive industries is to move towards a cloud-based solution as the architecture is scalable, but organization which have already invested on a on-premises infrastructure for data storage needs to seamlessly leverage these without costly and complex migrations or user disruptions. This paper describes end to end solution and design of global Flexible Data Solution which covers Data protection, Data Integrity and Data availability by Load balancing & failover management. Data is one of the assets for an organization but without a robust data management it can also most vulnerable. First approach would be to ensure data is secured, protected, available where and when it is needed. Having right strategy can detect problems and automatically provide insights about your data center, which will be beneficial in mitigating the risks in sensor data management. Using interface plugin, we can design scalable persistent storage for centralized workloads with operations including volume provisioning and deletion, snapshot creation and deletion, Data footprint change, Data Archival, Data Acceptance strategy, Mixed workloads (Windows/Linux) and incrementally add computation unit. With this approach sensor data can be stored and managed at the required performance with lowest cost to achieve desired results.