Optimizing Data Lakes for Advanced Analytics: A Comparative Analysis of Integration and Management Strategies

Abstract

The increasing prevalence of big data has driven the need for scalable and flexible data management solutions, positioning data lakes as a central element of modern data architectures. This paper explores the role of data lakes in enabling advanced data analytics by comparing various data integration and management techniques. It traces the evolution of data lakes, highlighting their significance in handling large-scale, diverse data processing tasks, including machine learning and real-time analytics. A comparative analysis is presented between traditional ETL (Extract, Transform, Load) and the more adaptable ELT (Extract, Load, Transform) method used in data lakes. The study also examines key challenges associated with data lake management, focusing on governance, quality, and security, while offering best practices to enhance performance. The findings indicate that although data lakes provide substantial benefits for advanced analytics, their effective deployment depends on robust data management and governance practices. The paper concludes by emphasizing the need to balance flexibility with control to maximize the value of data lakes in fostering business growth through analytics.