REAL-TIME ANOMALY DETECTION IN DISTRIBUTED SYSTEMS: LEVERAGING JAVA AND APACHE FLINK FOR ENHANCED DATA MANAGEMENT AND SYSTEM OPTIMIZATION IN ENTERPRISE ENVIRONMENTS

Abstract

The proliferation of distributed systems in enterprise environments has led to significant advancements in data processing, management, and real-time analytics. However, the complexity of these systems often introduces challenges related to system optimization and anomaly detection. Real-time anomaly detection has emerged as a critical component for ensuring system reliability, performance, and security in distributed architectures. This paper explores the integration of Java and Apache Flink for real-time anomaly detection in distributed systems, focusing on how these technologies can be leveraged to enhance data management and optimize system performance. Java, known for its robustness and scalability, combined with Apache Flink's capabilities for real-time stream processing, provides a powerful framework for detecting anomalies as they occur, thereby enabling proactive responses to potential system failures or security breaches. The paper delves into the architecture of distributed systems, the challenges of anomaly detection, and the specific features of Java and Apache Flink that make them suitable for this purpose. Additionally, it examines the implementation strategies, performance considerations, and the potential impact of such an approach on enterprise environments. The findings suggest that the integration of Java and Apache Flink offers a scalable, efficient, and flexible solution for real-time anomaly detection, ultimately leading to improved system reliability, reduced downtime, and enhanced data integrity in distributed systems.