Cloud-Based Microservices Architecture for Automotive Business Intelligence Platform

The client operates a legacy BI suite serving global automotive data consumers, with semi-automatic ETL pipelines, inconsistent data quality, and high maintenance costs consuming a majority of their budget. Performance bottlenecks and lack of scalability hinder timely and accurate analytics, impacting decision-making processes across multiple distributed teams. The need exists to modernize the system into a scalable, reliable, cloud-based architecture supporting advanced analytics and user-centric portals.

A large-scale automotive manufacturer or data provider with a global customer base, seeking scalable, high-performance BI solutions to enhance data processing, analytics, and reporting capabilities across distributed teams.

• Achieve a 15x or higher increase in system throughput to ensure real-time data processing and analytics.
• Migrate and process at least 5TB of historical data within a limited time window, ensuring accuracy and consistency through iterative validation.
• Replace legacy infrastructure with a microservices-based architecture leveraging cloud technologies such as Azure or equivalent.
• Implement automated data collection, cleaning, validation, and transformation pipelines for high data quality.
• Develop a centralized, scalable BI portal with intuitive visualizations and custom tools tailored to automotive industry needs.
• Enable semantic search capabilities supporting multiple languages and fast query responses via optimized indexing and translation mechanisms.
• Create APIs for third-party integration, facilitating external applications and clients to connect seamlessly.
• Set up an R&D environment for continuous testing and deployment of innovative BI solutions responsive to evolving user demands.
• Establish distributed development workflows under Agile frameworks, including SAFe, with synchronized team collaboration and regular planning cycles.

• Automated data collection, cleaning, and transformation pipelines for all incoming data sources.
• Metadata mapping and management system to handle diverse data schemas.
• High-performance search engine supporting multiple languages and semantic queries.
• Centralized core data storage utilizing scalable cloud databases such as Cosmos DB or equivalent.
• Data validation and visualization tools to ensure data quality and facilitate QA processes.
• APIs and SDKs for integration with third-party applications and client systems.
• A flexible, user-friendly BI portal with customizable dashboards and reporting tools.
• Development of an R&D center for testing new BI tools like dashboards, analytics apps, and data models.

• Data sources across multiple geographies and formats
• Machine learning models for predictive analytics
• Third-party apps for extended data consumption
• Existing legacy databases undergoing migration

• System scalability to handle a minimum of 15x throughput increase
• Data migration capacity of at least 5TB of historical data within 10 hours
• High search performance with instant semantic query responses
• Robust security and compliance measures for sensitive automotive data
• Resilience and high availability of the system across cloud regions
• Flexible API access for external application integration

The modernized BI solution is expected to significantly reduce system lag and maintenance costs, enabling real-time, accurate analytics that support strategic decision-making. Enhanced data quality and faster processing will foster better market positioning, increase customer satisfaction, and support business growth by providing scalable, flexible insights tailored to diverse automotive industry stakeholders.