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Development of a Scalable Data Management Platform for Automotive R&D Optimization

grapeup.com

Automotive

Challenges in Engine Test Data Management

The client struggled with managing vast volumes of sensor data from engine testing labs, leading to inefficiencies in data accessibility, inconsistent formats, and limited usability for data science teams. This hindered engine performance optimization, increased maintenance costs, and compromised safety analysis.

About the Client

A leading automotive manufacturer focused on optimizing engine performance and safety through advanced data analytics.

Objectives for Data Management Platform Development

Centralize and standardize engine test data collection for R&D teams
Enable real-time and batch data processing capabilities
Automate data normalization to ensure consistency across teams
Enhance data accessibility for data scientists and engineers
Implement agile methodologies for iterative platform improvements

Core System Functionalities and Features

Real-time data streaming from engine testing sensors
Automated data normalization and format standardization
Batch processing for historical data analysis
User access controls for secure data sharing
Agile project management tools (e.g., JIRA integration)

Preferred Technologies

Apache Kafka for real-time data streaming

Apache Spark for batch processing

Cloud-based data lake architecture

JIRA for agile project management

Required System Integrations

Existing engine testing lab sensors
Client's internal data storage systems
Third-party analytics tools (e.g., Python/R libraries)
User authentication and authorization systems

Non-Functional Requirements

Scalability to handle petabytes of sensor data
High availability for uninterrupted data processing
Data encryption and compliance with automotive industry standards
Low-latency real-time data processing

Expected Business Impact of Enhanced Data Management

The platform will reduce engine testing and maintenance costs by 20-30%, accelerate R&D cycles through faster data insights, and improve engine performance and safety metrics. Standardized data workflows will foster cross-team collaboration, while agile delivery ensures continuous alignment with evolving business needs.