Data streaming for agile operations

Near real-time vehicle data processing for a leading European automotive OEM. A MakoLab solution
Summary
The MakoLab data streaming services team partnered with a leading European automotive OEM to modernise their vehicle data hub. We designed and implemented a centralised, scalable architecture that leverages Apache Pulsar and Flink for near real-time, event-based data processing. The solution integrated over 30 producing systems and more than 100 consuming systems, while addressing legacy data quality issues through an extensive cleanup. Our flexible model ensures reliable data flow, regulatory compliance and seamless integration. The result is a high-performance platform capable of processing millions of events, delivering low-latency access to vehicle history data and supporting responsive, efficient operations across mobile apps and service centres.
Client
Industry
Automotive
Service
Damage claim management system
Deliverables
time optimisation, lower costs, enhanced fraud detection

Details

The challenge

The project presented several challenges. The first was to ensure the reliable processing of vast volumes of data throughout the entire vehicle lifecycle, from production and usage, including telemetry from connected vehicles, to end of life, for newly manufactured and older vehicles spanning more than two decades. The legacy systems were unable to keep pace with the growing data demands, resulting in inefficiencies and inconsistent or incomplete data across sources.

Regulatory compliance was another challenge. Our client is obliged to meet evolving legal and data protection requirements, which adds even more complexity to data management and increases operational risk.

Finally, delays and inaccuracies in data availability were causing falling customer satisfaction, consuming too much time and generating excessive costs. All of this underscored the urgent need for a more robust and scalable data infrastructure.

The solution

To meet these challenges, we: 

·     created a centralised, scalable system architecture for high-quality, near real-time, event-based vehicle data processing;

·     built a solution that would fetch data from over thirty producing systems and expose it to more than one hundred consuming systems;

·     addressed long-standing data quality issues by performing a crucial, and extensive, data cleanup during the migration from the legacy systems.

Our efforts therefore focused on building a flexible model capable of adapting to changing operational and market conditions and ensuring reliable data flow while providing improved accuracy and smooth integration across the ecosystem.

Key results

·    The deployment of a sustainable new architecture supporting fast-growing business needs through system improvements

·    The platform now processes millions of events, delivering immediate access to high-quality vehicle history data

·    Reliable data is made available to end users via mobile apps and service centres, enhancing transparency and the user experience

·    The system ensures a low latency of under 500ms and high availability, supporting real-time operations

·    Full compliance with EU regulations, including the GDPR and CAFE, is maintained through robust data governance and architecture.