Anomaly detection is a critical process in identifying deviations from expected patterns in datasets, playing an essential role in early problem detection, risk management, system health monitoring, quality assurance, and safety across various sectors. It encompasses a range of techniques, from statistical methods like Z-score and Grubbs' Test to machine learning approaches including supervised, unsupervised, and semi-supervised learning, as well as time-series and distance-based analysis. Despite challenges such as imbalanced datasets, high dimensionality, evolving patterns, and computational complexity, anomaly detection remains vital for maintaining system integrity and preventing fraud or disasters. PubNub Illuminate enhances this detection capability by offering real-time data ingestion, machine learning integration, historical data analysis, and visualization tools, thus allowing for efficient monitoring and response to anomalies across applications like fraud detection, IoT device monitoring, network security, and operational monitoring.