Real-time anomaly detection: algorithms, use cases & SQL code

Anomaly detection, also known as outlier detection, is a crucial technique for identifying data points that significantly deviate from expected patterns across various domains. This practice has evolved from traditional, static dataset analysis to real-time applications, driven by the increasing volume and velocity of data. Real-time anomaly detection is essential for systems handling continuous data streams, such as credit card fraud detection, IoT monitoring, and cybersecurity, with techniques ranging from simple out-of-range checks to complex machine learning algorithms. The field is divided into supervised and unsupervised methods, each with distinct advantages; supervised methods rely on labeled data for training, while unsupervised methods identify anomalies in real-time without predefined labels. Recent advancements in real-time databases, like Apache Druid and ClickHouse®, enable high-speed analytical queries, facilitating the implementation of real-time anomaly detection systems. Examples include SQL-based algorithms for out-of-range, timeout, rate-of-change, interquartile range (IQR), and Z-score anomaly detection, which help detect anomalies nearly instantaneously while adapting to changing data trends.