Modernization and Optimization of Data-Driven Analytics System for a Large-Scale Data Science Provider

The client faces challenges with an outdated, monolithic analytics application that suffers from system bugs, performance bottlenecks, and limited scalability. The existing architecture hampers data processing efficiency, increases operational costs, and reduces user experience quality. Additionally, the current system's direct database connections limit flexibility and future growth potential.

A global enterprise specializing in data science and artificial intelligence solutions, serving various sectors including retail, financial services, healthcare, and public sector organizations, with a large cross-functional tech team seeking system modernization.

• Achieve a seamless migration from legacy technology stack to a modern, scalable architecture within a targeted timeframe.
• Improve system stability, reduce bugs, and enhance overall performance and responsiveness.
• Implement an intermediary layer with robust metrics capabilities to facilitate better error handling, performance tracking, and maintenance.
• Reduce operational costs through optimized resource utilization and infrastructure improvements.
• Enable the system to handle increased data volumes and user loads, supporting potential revenue growth and new service offerings.
• Enhance maintainability and debugging efficiency via comprehensive documentation and structured system architecture.

• Migration of existing analytics application to a contemporary technology stack (e.g., .NET, Angular).
• Development of a modular, scalable backend architecture incorporating an intermediary metrics library for data interactions.
• Implementation of thorough end-to-end testing environments, including unit, integration, and system testing.
• Integration of data pipelines and external data sources for comprehensive analytics capabilities.
• Enhanced user interface facilitating efficient data visualization and interaction.
• Robust error handling, logging, and debugging features using performance metrics.

• External data sources and APIs for data ingestion
• Database systems with decoupled connection layers
• Existing data pipelines and analytics modules
• Performance monitoring and logging tools

• System scalability to support increasing data volumes and concurrent users
• Response times optimized for end-user interactions (target response time: <2 seconds)
• High availability and fault tolerance to minimize downtime
• Security measures ensuring data integrity and confidentiality
• Maintainability through comprehensive documentation and modular architecture

The project aims to reduce operational costs by improving resource efficiency, enhance system stability and performance leading to better user experience, and support increased data processing capacity to facilitate business growth. Expected outcomes include faster response times, lower system downtime, and a scalable infrastructure capable of supporting expansion in user base and data volume, thereby indirectly boosting revenue potential.