Digital Transformation of a Large Telematic Service

Analyzing critical information about vehicle and driver parameters in real-time.

Yuliya Sychikova
COO @ DataRoot Labs
02 Jul 2020
4 min read
Digital Transformation of a Large Telematic Service
Client Services
Industries
Logistics
Next Case
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Summary

  • Our client is a telematics service used for truck fleet management. Technically, their solution is a mix of sophisticated software and hardware for collecting and analyzing critical information about vehicle and driver parameters in real-time, allowing customers to manage their fleet with a high degree of efficiency and at the lower cost.
  • The client strove to augment driver's safety and security and enhance the driving experience by digitally transforming its obsolete software into a modern platform powered by AI, able to track the large number of fleet vehicles in real-time.
  • Our team designed and implemented different parts of the final product, including APIs, web-interfaces and mobile apps, with the main challenge to create the system which receives the data from hundreds of thousands of monitoring devices in real-time, working 24/7.
9
Months
7
Engineers
7+
Technologies

Tech Stack

Akka
Apache Spark
Cassandra
GlusterFS
Kafka
PostgreSQL
Scala

Timeline

2 Weeks
Solution Architecture Design
Solution Architect
8 Weeks
Data Warehouse Configuration
Data Engineer
10 Weeks
Realtime Streaming Pipeline Development
Data Engineer
12 Weeks
Training & Tuning Cycle pt.1
Data Engineer
15 Weeks
Web / App Platform Development
App Developer
Backend Developer
Frontend Developer
6 Weeks
Training & Tuning Cycle pt.2
Data Engineer
4 Weeks
Integration & Testing & Deployment
Backend Developer
Data Engineer
Deep Learning Researcher
Dev Ops

Tech Challenge

  • Tracking a large vehicle fleet requires integration of large amounts of sensor data into a single pipeline.
  • Making tracked data available to compliance units within seamless interface, at a lower cost.
  • Data has to be processed and accessible in near real-time fashion.

Solution

  • All the data is stored in the scalable cluster in a fault-tolerant manner (Cassandra).
  • The mobile app has a real-time access to the information from each connected monitoring device.
  • System automatically produces reports for each fleet vehicle, via collecting data from analytical indicators built by our team.
  • Huge amounts of data are processed fast due to Apache Spark cluster; micro-services were built as an isolated Docker containers.
  • Metrics gathered in real-time are numerous and detailed (i.e. number of stops, start and end time of the journey, mileage, speed, idle time, route history, geofences, fuel consumption, engine temperature, etc.) They are used to create automatic alerts about accidents, critical situations, theft and other situations.

Impact

  • We have developed a sophisticated platform for collecting and analyzing critical information about vehicle and driver parameters in real-time.
  • The final solution allowed customers to manage their fleet with a high degree of efficiency and at a lower cost.
  • The platform receives the data from hundreds of thousands of monitoring devices in real-time, working 24/7.

Author

Yuliya Sychikova
COO @ DataRoot Labs
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.

Co-Authors

Ivan Didur
CTO @ DataRoot Labs
offices map
Kyiv (HQ)
Max Frolov
CEO @ DataRoot Labs
Tel Aviv
Ivan Didur
CTO @ DataRoot Labs
Los Angeles
Yuliya Sychikova
COO @ DataRoot Labs
builds and implement AI-powered systems across different verticals to help our clients operate effectively.