DESIGNING EFFICIENT DATA LAKE ARCHITECTURES FOR LARGE-SCALE MULTI-TENANT CLOUD PLATFORMS

Abstract

This research paper explores the design of efficient data lake architectures tailored for large-scale multi-tenant cloud platforms. As organizations increasingly adopt cloud computing, managing vast amounts of data across multiple tenants presents significant challenges. This study reviews the evolution of data lake architectures, highlighting the specific issues related to multi-tenant environments, such as scalability, security, and cost efficiency. We propose a novel architecture that addresses these challenges by optimizing data flow, ensuring data isolation, and integrating seamlessly with existing cloud infrastructure. The proposed design is evaluated against traditional architectures through simulation, demonstrating significant improvements in performance and cost-effectiveness. This paper aims to contribute to the ongoing development of scalable and secure data management solutions in cloud-based environments.