Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Intelligent anomaly detection in real-time big data engineering represents a critical advancement in how organizations identify and respond to unexpected patterns across high-velocity data streams. This article explores the evolution from retrospective analysis to proactive defense mechanisms enabled by the convergence of stream processing technologies and advanced machine learning techniques. The article encompasses the fundamental categorization of anomalies in streaming environments—point, contextual, and collective—alongside complementary detection approaches that range from rule-based systems to sophisticated deep learning architectures. Key challenges, including data velocity management, concept drift adaptation, alert fatigue mitigation, and system scalability, are addressed within the context of production implementations. The article extends to the evolving ecosystem of stream processing engines, specialized detection libraries, and monitoring infrastructure supporting these capabilities. Through sector-specific case studies spanning financial services, manufacturing, cybersecurity, and healthcare, the article demonstrates the transformative impact of real-time anomaly detection across industries. Finally, emerging trajectories, including the integration of explainable AI, federated learning approaches, edge computing deployment, and generative AI applications, are explored as future directions that will reshape the field.