Introduction of Azure Data Explorer and Azure Cognitive Services Anomaly Detector
The Anomaly Detector API enables you to check and detect abnormalities in your time series data without having to know machine learning. The Anomaly Detector API's algorithms adapt by automatically finding and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API decides boundaries for anomaly detection, expected values, and which data points are anomalies.
Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.
What are the new functions in ADX for anomaly detection?
The function series_uv_change_points_fl() finds change points in time series by calling the Univariate Anomaly Detection API, part of Azure Cognitive Services. The function accepts a limited set of time series as numerical dynamic arrays, the change point detection threshold, and the minimum size of the stable trend window. Each time series is converted into the required JSON format and posts it to the Anomaly Detector service endpoint. The service response has dynamic arrays of change points, their respective confidence, and the detected seasonality.
These two functions are user-defined tabular functions applied using the invoke operator. You can either embed its code in your query (ad hoc) or you can define it as a stored function in your database (persistent).
Where to use these new capabilities?
These two functions are available to use either in Azure Data Explorer website or in Kusto. Explorer application.
Getting started is simple!
Create an ADX Cluster in Azure portal, after the resource is created successfully, go to the resource and create a database.