Investigating the Role of Data Lakes in Facilitating Advanced Data Analytics: A Comparative Study of Data Integration and Management Strategies

Abstract

The proliferation of big data has necessitated the development of scalable and flexible data management solutions, with data lakes emerging as a key component in modern data architectures. This paper investigates the role of data lakes in facilitating advanced data analytics by comparing various data integration and management strategies. The study begins by exploring the evolution of data lakes, emphasizing their importance in supporting diverse and large-scale data processing needs, including machine learning and real-time analytics. The paper then provides a comparative analysis of data integration approaches, contrasting traditional ETL (Extract, Transform, Load) with the more flexible ELT (Extract, Load, Transform) strategy commonly employed in data lakes. It discusses the challenges of managing data lakes, particularly in areas such as data governance, quality, and security, and offers best practices for optimizing their performance. The findings suggest that while data lakes offer significant advantages in supporting advanced analytics, their successful implementation requires careful attention to data management and governance practices. The paper concludes by highlighting the importance of balancing flexibility with control to fully leverage the potential of data lakes in driving business value through advanced analytics