Authors: Nabilah Hassan
Abstract: Distributed data systems have become a fundamental component of modern computing, enabling the storage, processing, and management of large-scale data across multiple interconnected nodes. These systems are designed to provide high scalability, fault tolerance, and availability, making them suitable for handling the growing demands of big data and real-time applications. By distributing data and computational tasks across different locations, organizations can achieve improved performance and reliability compared to centralized systems. This paper explores the architecture and key characteristics of distributed data systems, including data partitioning, replication, and consistency models. It also examines the role of technologies such as distributed databases, cloud computing platforms, and big data frameworks in supporting efficient data processing. The study highlights major application areas including finance, healthcare, e-commerce, telecommunications, and scientific research. Additionally, it discusses critical challenges such as data consistency, network latency, fault tolerance, and security concerns, along with potential solutions. The findings emphasize that distributed data systems are essential for managing large-scale, data-intensive applications in today’s digital world.
DOI: https://doi.org/10.5281/zenodo.20283696
