The auto industry is projected to be the second largest generator of data by next year. One innovative manufacturer saw the opportunity to leverage massive amounts of data telematics produced by cars and drivers to plan new features and gain competitive advantage.
Collect, store & analyze petabytes of data globally from millions of vehicle sensors, customers, dealers and social media.
Commercial legacy appliance costing in excess of $1.2 Million could not handle the workload.
$125K OpenStack cluster set to scale from POC to 10-rack system in 6 months, easily handling complex data analysis.
Proving The System
They began with a small proof of concept (POC) that would validate the car cloud on OpenStack comparing use cases against the company’s legacy appliance. The POC started by defining metrics and KPIs to set quantifiable objectives that would help make the case to senior executives.
In three short weeks the team built the cloud and tested it with several practical use cases, including in-car mapping applications and back-end big data analytics with telematics data loaded into Hadoop.
During the test, the team loaded and updated mapping information in real time from vehicles to the cloud infrastructure. They also completed comparison tests to demonstrate the ability to scale and meet fluctuating workloads in a distributed environment. The automotive company preloaded vehicle telematics data into Hadoop, then transformed the data via map reduce jobs, and ran several analytics use cases to validate the accuracy and performance.
The comparison between the OpenStack car cloud and the company’s legacy appliance was dramatic. In the use case with telematics data and Hadoop, the performance on the OpenStack car cloud clocked in at 40 minutes (before optimization), while the same job on the legacy appliance was unable to compute the function.
Planned Out Over Three Phases
Building out a production infrastructure with big data analytics for existing applications on a single rack consisting of 10 compute nodes, 48 TBs of block storage and 72 TBs of object storage.
Expand solution to focus on big data analytics for production vehicle telematics and in-car services in limited regions, upgrading the architecture and expanding the car cloud with seven racks consisting of 64 compute nodes, 181 TBs of block storage and 859 TBs of object storage.
Expanding the car cloud globally with a focus on in-car services with 26 racks consisting of 192 compute nodes, 1 PB of block storage and 5 PBs of object storage.