Anomaly Detection
Anomaly detection is the process of identifying data points, events, or patterns that deviate from expected behavior. It is used to catch fraud, predict equipment failure, spot security threats, and flag operational problems.
How anomaly detection uses history
At its core, anomaly detection is about knowing what normal looks like and noticing when something is not. That can be a simple threshold, a statistical model, or a machine learning approach, but all of them depend on having a good picture of normal, which comes from history.
The quality of detection depends on the quality and depth of that historical data. If you only keep a few days of low-resolution history, your sense of normal is thin and your detection is weak. Rich, full-resolution history makes for sharper detection and fewer false alarms.
It shows up across domains: unusual transactions in finance, abnormal vibration in machinery, traffic spikes in infrastructure, intrusions in security.
How Arc handles Anomaly Detection
Arc gives anomaly detection a deep, full-resolution history to learn from, stored affordably as Parquet. Models and queries can scan long baselines fast, which is what sharp detection with few false positives requires.
Arc is a high-performance columnar database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.