fleet tracking using CrateDB tracks automotive vehicle fleets in real-time.

Real-time tracking of automotive vehicle fleets
2,000 data points–per car–per second
Machine learning to predict vehicle malfunctions

Clickdrive, an internet-enabled onboard vehicle monitoring solution, uses CrateDB to enable storage and analysis of up to 2,000 data points per second, per car. Leveraging a unique combination of precision telematics and predictive maintenance capabilities, Clickdrive can predict a breakdown before it happens – helping operators improve the safety, efficiency, and economy of their commercial fleets.

For example, within three months of use, one Clickdrive customer was able to identify issues in 50% of the vehicles that were on the road – issues that were otherwise unknown to the drivers and mechanics. In predicting these issues, not only is safety improved, but maintenance costs were lowered by as much as 20% – placing less pressure on mechanics and maintenance facilities, while dramatically reducing unnecessary use of replacement parts and other consumables.

Clickdrive Fleet utilization dashboard

The devices monitor everything from the condition of the vehicle, to the engine, and the critical subsystems. The result of which is an unbelievable torrent of data.

"Most of the cars in our customers’ fleets are in use almost 24 hours a day, and we need to store and analyze the massive amounts of data they generate in real time. We tried a few different SQL and NoSQL databases, and CrateDB offered the best combination of high performance, scalability, and ease-of-use."

Mark Sutheran


Using CrateDB as part of their IoT stack has enabled Clickdrive to to ingest and analyze vehicle data from large vehicle fleets in real-time, without the costs associated with alternative database offerings.

Clickdrive using CrateDB: tracking dashboard

"CrateDB’s ability to query enormous amounts of data expands the realm of what’s possible with Clickdrive. As an example, we can use the vehicle and environmental data stored in CrateDB to quickly build a complete 3D reconstruction of an accident — showing everything from road conditions, to driver orientation, point of impact, and speed."

Mark Sutheran



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