Maximizing Cost Efficiency and Scalability in Cloud-Based Data Lakes: Opportunities and Challenges

Abstract

The integration of data lakes with cloud computing platforms has become a key strategy for organizations aiming to enhance data management while achieving cost efficiency and scalability. Cloud-based data lakes offer a flexible and scalable solution for storing and processing large volumes of diverse data, leveraging the elastic resources and advanced services provided by platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud. This paper investigates the cost efficiency and scalability implications of this integration.  Drawing on literature and case studies, the paper examines the cost benefits, including pay-as-you-go pricing, cost management techniques, and potential long-term savings. It also addresses challenges such as unexpected expenses, data egress fees, and managing hybrid and multi-cloud strategies. In terms of scalability, the study explores the advantages of elastic scaling, performance optimization, and cloud-native architectures, while also considering the challenges of maintaining data governance and quality in highly scalable environments. The findings highlight the necessity of adopting best practices in cloud architecture, automation, and governance to fully capitalize on the benefits of cloud-based data lakes. This research offers practical insights for organizations seeking to optimize their cloud data lakes for both cost efficiency and scalability.