Skylar Analytics contains a set of tools that lets you view, analyze, and use the data that SL1 gathers and sends to the Skylar AI engine. Skylar Analytics insights are presented in the SL1 user interface, in a ScienceLogic-hosted instance of Apache Superset, and in the Skylar AI API.
Skylar Analytics includes the following components:
- Data Visualization contains SQL-based dashboards and charts based on data gathered by Skylar AI and SL1. Data Visualization is achieved using a ScienceLogic-hosted instance of Apache Superset or with your own third-party tool.
- Data Exploration enables third-party tools that use the Open Database Connectivity (ODBC) interface to access the metric data from Skylar AI. This component lets you use ODBC to connect Skylar AI data with applications like Tableau, Microsoft Power BI, or other business intelligence tools.
- Anomaly Detection uses Skylar AI to identify unusual patterns that do not conform to expected behavior. Anomaly Detection provides always-on, unsupervised, machine-learning-based monitoring that automatically identifies unusual patterns in the real-time performance metrics and resource data that it observes. Anomalies do not necessarily represent problems or events to be concerned about; rather, they represent unexpected behavior that might require further investigation.
- Predictive Alerting helps to avoid problems such as file systems running out of space, hosts running out of memory, or issues with network reliability due to oversubscription. The alerts are generated in advance of the problem and can provide days, weeks, or months of notice depending upon the conditions.
This video provides an overview of the different features of Skylar Analytics: https://player.vimeo.com/video/990317575?h=74e1aca2bf