Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently required for users to benefit from such services.

Because distributed systems are capable of handling large amounts of data, software engineers have proposed several techniques to advance distributed data processing.

Data Mesh, the focus of this paper, explores the idea of distributed data ownership, allowing teams to rely less on a centralized IT team and preserve context, while the organization’s IT …

– Four system architecture approaches ranging from sharing everything (used by non distributed systems) to sharing mem-ory, disk, or nothing. In a shared disk architecture, all CPUs can …

Distributed vs Parallel DBMS Parallel DBMS Goal: improve performance through parallelization Data distribution is governed solely by performance Typically all machines are on the same …

In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo‐distributed data centers.

It explores the technological foundations of distributed processing in finance, including cloud-native architectures, parallel computing frameworks, and decentralized data management …