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Revamping the Data Platform for market leading car sharing provider:
Enabling Advanced Machine Learning and Experiments

Our team together with the data team of a market leading shared mobility provider embarked on an ambitious project to modernize their data platform. This endeavor aimed to empower even more people with data-intensive machine learning solutions, propelling the company into a new era of sustainable mobility while offering an unparalleled service to their users.

 

Data Max helped the shared mobility provider reengineer and scale the existing data infrastructure and workflows to cover more data-intensive applications and be ready for future challenges.

Technologies: AWS, EKS, Airflow, Kubernetes, Cluster Autoscaler, Snowflake, dbt

Challenge

Being in the market leader position for shared mobility led to the rapid accumulation of vast amounts of data in the shared mobility provider’s platform. As the company evolved, it faced the challenge of managing and extracting valuable insights from this ever-expanding data ecosystem. The existing data infrastructure struggled to keep pace with the growing demands, hindering the development of innovative data-driven solutions and potentially compromising the overall customer experience.

Challenge

Solution

Solution

Recognizing the crucial role of a robust data platform in shaping the future of sustainable mobility, the shared mobility provider collaborated with our team to embark on a transformative journey. Our mission was to reengineer and scale their data infrastructure and workflows, ensuring they could accommodate more data-intensive applications while staying resilient to future challenges.

 

We introduced a combination of Kubernetes, Cluster Autoscaler, and Airflow, to enable the users of the data platform to allocate resources based on their needs and to deploy their use cases.

Furthermore, to increase autonomy inside the data team, we developed a new deployment process that would allow every engineer to deploy their solutions without being affected by the other cases that are running on the platform.

Result

The modernization of the data platform fundamentally improved the amount of provided services and the speed in which these services are provided to the users. The project's user-oriented approach culminated in the following remarkable outcomes:

 

  • Expanded Services: The revamped data infrastructure enabled the provider to launch a myriad of new data-intensive machine learning use cases. From dynamic pricing models to predictive maintenance for vehicles, the company diversified its offerings, delighting users with cutting-edge solutions.

 

  • Improved Operational Efficiency: By streamlining data workflows and optimizing infrastructure, the shared mobility provider experienced improved operational efficiency and cost-effectiveness, further empowering their growth in the market.

 

  • Future-Readiness: The new data platform's scalability and adaptability ensured that the shared mobility provider remained poised to tackle future challenges, staying ahead in the ever-evolving mobility landscape.

Result

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