Overview

The Time Series Analytics microservice provides the analytics capabilities for a time series use case.

It is a powerful, flexible solution for real-time analysis of time series data. Built on top of Kapacitor, it enables both streaming and batch processing, seamlessly integrating with InfluxDB for efficient data storage and retrieval.

What sets this microservice apart is its support for advanced analytics through User-Defined Functions (UDFs) written in Python. By leveraging the Intel® Extension for Scikit-learn*, you can accelerate machine learning workloads within their UDFs, unlocking high-performance anomaly detection, predictive maintenance, and other sophisticated analytics.

The key features include:

• Bring your own Data Sets and corresponding User Defined Functions(UDFs) for custom analytics: Easily implement and deploy your own Python-based analytics logic, following Kapacitor’s UDF standards.

• Seamless Integration: Automatically stores processed results back into InfluxDB for unified data management and visualization.

• Model Registry Support: Dynamically fetch and deploy UDF scripts, machine learning models, and TICKscripts from the Model Registry microservice, enabling rapid customization and iteration.

• Versatile Use Cases: Ideal for anomaly detection, alerting, and advanced time series analytics in industrial, IoT, and enterprise environments.

For more information on creating custom UDFs, see the Kapacitor Anomaly Detection Guide