Integrating AI-Driven Anomaly Detection in Data Lakes for Enhanced Data Quality and Performance Optimization

Abstract

In the age of big data, organizations rely on data lakes to store vast volumes of diverse data, enabling flexible analytics and insights. However, ensuring data quality and optimizing performance within these data lakes poses significant challenges, including data inconsistencies, redundancy, and slow query responses. This paper explores the integration of AI-driven anomaly detection techniques in data lakes to enhance data quality and optimize performance. By employing machine learning algorithms for real-time anomaly detection, organizations can proactively identify and resolve data quality issues, thereby improving the overall efficiency and reliability of data lake operations. The paper presents various anomaly detection methods, discusses implementation strategies, and highlights case studies showcasing successful applications.