White Paper

Real-time Anomaly Detection at Scale: 19 Billion Events per day


Anomaly detection is a method used to detect unusual events in an event stream. It is widely used in a range of applications such as financial fraud detection, security, threat detection, website user analytics, sensors, IoT, system health monitoring, etc. Streaming data (events) from these applications are inspected for anomalies or irregularities and when an anomaly is detected, alerts are raised either to trigger an automated process to handle the exception or for manual intervention.

Anomaly detection applications typically compare inspected streaming data with historical event patterns, raising alerts if those patterns match previously recognized anomalies or show significant deviations from normal behavior. These detection systems utilize a stack of solutions that often include machine learning, statistical analysis, and algorithm optimization, and that leverage data-layer technologies to ingest, process, analyze, disseminate, and store streaming data.

Fill out the form on the right to download the white paper to understand how we built a streaming data pipeline application that is able to overcome the hurdles of mass-scale anomaly detection with powerful open source data-layer technologies delivered through our fully managed platform.