Your Data: Events, Metrics, Anomalies, and Security

This topic describes the external events and metrics that Moogsoft can ingest, how Moogsoft detects metric anomalies in real time, and the Moogsoft features that keep your data secure.

Event and metric ingestion

The first step in the data pipeline is to ingest monitoring metrics and events of interest from your infrastructure. Moogsoft includes the following integrations:

  • The Moogsoft Collector, an easy-to-install agent that collects server, Docker, and other metrics on Linux servers. You can deploy collectors on nodes throughout your physical, virtual, and cloud infrastructure.

    The Collector includes an extensible framework that supports data collection from additional services and platforms.

  • An AWS CloudWatch integration for ingesting cloud metrics.

  • A Metric API for ingesting time series metrics from external monitors.

  • An Events API for ingesting data from external tools such as AppDynamics, New Relic, and DataDog.

    The metric and events schemas are both highly generic and flexible. Each schema has a small set of required data fields and support for additional fields.

Anomaly detection features

Moogsoft uses advanced analytics to identify performance anomalies on each time series metric. Each metric anomaly is considered an event of operational significance.

Moogsoft uses the following anomaly detectors.

  • The Adaptive detector identifies anomalies based on a statistical calculation against a median absolute deviation, which varies over time and determines the high and low thresholds.

  • The Threshold Detector identifies anomalies based on a fixed upper and/or lower threshold.

  • Bitwise and False detectors identify anomalies use bitmasks and Booleans to evaluate whether a system is running correctly.

The Moogsoft Collector detects anomalies immediately on the installed host. This reduces the latencies involved in transferring and analyzing raw data from many different sources in a central location.

You can fine-tune how Moogsoft detects anomalies for individual metrics with special characteristics. For example, you might want to fine-tune the anomaly-detection logic for metrics with very large or very small data ranges. See Customizing Anomaly Detection for Individual Metrics (Advanced).

Security features

Moogsoft adheres to all cloud-provider security practices such as the Amazon Shared Responsibility Model. For more information, see moogsoft.com/security.

Watch a video on Anomaly Detection Models in Moogsoft.