Optimizing Data Access and Loss Prevention Mechanisms in Big Data Cloud Architectures

Abstract

In today’s data-driven landscape, organizations increasingly rely on cloud architectures to manage vast amounts of information, making the optimization of data access and loss prevention mechanisms paramount. This paper explores effective strategies for enhancing data accessibility while minimizing risks of data loss in Big Data cloud environments. It examines key concepts such as data access control mechanisms, encryption, backup solutions, and the application of artificial intelligence (AI) for predictive analytics. The paper also highlights the role of multi-cloud strategies in ensuring resilience and flexibility in data management. By focusing on best practices and emerging technologies, this research aims to provide insights into building secure and efficient cloud-based Big Data architectures that meet the evolving needs of businesses.