Digital Asset Data Lakehouse. The concept based on a blockchain research center
Raul Cristian Bag
- Year
- 2025
- Access
- Open access
Abstract
In the rapidly evolving landscape of digital assets and blockchain technologies, the necessity for robust, scalable, and secure data management platforms has never been more critical. This paper introduces a novel software architecture designed to meet these demands by leveraging the inherent strengths of cloud-native technologies and modular micro-service based architectures, to facilitate efficient data management, storage and access, across different stakeholders. We detail the architectural design, including its components and interactions, and discuss how it addresses common challenges in managing blockchain data and digital assets, such as scalability, data siloing, and security vulnerabilities. We demonstrate the capabilities of the platform by employing it into multiple real-life scenarios, namely providing data in near real-time to scientists in help with their research. Our results indicate that the proposed architecture not only enhances the efficiency and scalability of distributed data management but also opens new avenues for innovation in the research reproducibility area. This work lays the groundwork for future research and development in machine learning operations systems, offering a scalable and secure framework for the burgeoning digital economy.
Keywords
Related papers
A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing
Qiang Cui, Chuan Yu, Daoqian Yang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
Geometric digital twin: A digital and intelligent model for aero-engine assembly accuracy prediction
Ke Shang, Xin Jin, Teli Xu +4 more
Robotics and Computer-Integrated Manufacturing · 2026
Revolutionizing Industries Through AI-Driven Robotics
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
Design and dynamic performance prediction of a novel large-aperture offset-feed deployable antenna
Chuang Shi, Tianming Liu, Ning Xue +6 more
Aerospace Science and Technology · 2026