Logo
  • Cases & Projects
  • Developers
  • Contact
Sign InSign Up

Here you can add a description about your company or product

© Copyright 2025 Makerkit. All Rights Reserved.

Product
  • Cases & Projects
  • Developers
About
  • Contact
Legal
  • Terms of Service
  • Privacy Policy
  • Cookie Policy
Data Management and Application Modernization for Fleet Solutions Provider
  1. case
  2. Data Management and Application Modernization for Fleet Solutions Provider

Data Management and Application Modernization for Fleet Solutions Provider

kandasoft.com
Logistics

Core Data and Application Challenges in Fleet Management Infrastructure

The client manages over 50 siloed Microsoft SQL databases, limiting effective data sharing and integration across systems. They also contend with high costs due to storing IoT data without a centralized warehousing solution, and their applications are built on outdated technology stacks, hindering performance and scalability. Additionally, they face difficulties integrating new data sources and modernizing their user interfaces to meet current technological standards.

About the Client

A large fleet management and vehicle diagnostics company serving diverse clients including top-tier fleets, OEMs, dealerships, municipal agencies, and military organizations, with a complex data infrastructure and legacy applications.

Key Goals for Data and Application System Enhancement

  • Implement a comprehensive data warehousing solution to centralize IoT and vehicle data, enhancing data sharing and storage efficiency.
  • Develop robust APIs to improve backend service integration and streamline operations.
  • Upgrade legacy applications from older frameworks (.NET 4.6 and AngularJS) to modern, scalable technologies (.NET Core and Angular 12/13).
  • Modernize the user interface for better user experience and alignment with current frontend standards.
  • Achieve improved system efficiency, reduced data storage costs, and increased scalability to support future growth.

Core Functional System Requirements for Data and Application Modernization

  • A centralized data warehouse optimized for IoT and vehicle diagnostics data management.
  • APIs supporting real-time data access, system integration, and service orchestration.
  • Application upgrade pathways from legacy to modern frameworks, ensuring backward compatibility and future scalability.
  • Frontend enhancement to transition from AngularJS to Angular 12/13, enabling a responsive and intuitive user interface.

Preferred Technologies and Architectural Approaches

Data warehousing solutions (e.g., cloud-based or on-premises data lake/storage)
.NET Core for backend application development
Angular 12/13 for frontend application modernization
RESTful API architecture for system integration

External Systems and Data Source Integrations

  • IoT data sources from vehicle diagnostics devices
  • Legacy application interfaces for phased migration
  • External data sharing platforms or APIs as needed for client ecosystem compatibility

Non-functional System Requirements

  • Scalability to support increasing data volumes and user load
  • High availability and disaster recovery capabilities
  • Data security and compliance standards adherence
  • Performance benchmarks for API response times and data processing speeds

Business Impact and Project Outcomes

The project aims to significantly enhance data sharing capabilities, reduce storage and operational costs, modernize key applications to improve performance and user experience, and position the client for scalable future growth. Expected outcomes include improved system efficiency, lower total cost of ownership, and a more agile and integrated fleet management solution.

More from this Company

Enhanced Preference-Based Search Platform for Retail eCommerce
Development of a Scalable Prescription Management Software for Pharmacist Operations
Cloud-Native SaaS Platform Modernization for Tax Consulting Firm
Development of a Mobile Ethics and Compliance Information Platform for Field Sales Teams
Developing an AI-Driven Media Bias Analysis Platform for Enhanced News Transparency