Docs » Key concepts in Splunk Infrastructure Monitoring

Key concepts in Splunk Infrastructure Monitoring 🔗

In the following table, learn about key concepts in Splunk Infrastructure Monitoring to help you get the most out of your Splunk Infrastructure Monitoring experience.

Concept

Description

Data link

A dynamic link available for properties that can take you to a Splunk Infrastructure Monitoring dashboard or an external system, such as a Splunk instance or a custom-defined URL.

Navigator

A set of screens that lets you monitor instances of your integrations.

Resolution

Resolution refers to either one of the following:
- Data collection intervals, also known as native resolution.
- Intervals at which data points are displayed on a chart, also known as chart resolution.

SignalFlow

The statistical computation engine of Splunk Observability Cloud.

Virtual metrics

A unified format of data transformed and returned by Splunk Infrastructure Monitoring.

Resolution 🔗

Resolution refers to either one of the following:

  • Data collection intervals, also known as native resolution. To learn more, see Native resolution.

  • Intervals at which data points are displayed on a chart, also known as chart resolution. To learn more, see Chart data resolution.

If your organization uses a data points per minute (DPM) subscription plan based on the rate at which you’re sending data points to Splunk Infrastructure Monitoring, see Resolution and data retention in Splunk Infrastructure Monitoring (DPM plans only).

SignalFlow 🔗

SignalFlow is the statistical computation engine at the heart of Splunk Observability Cloud. You can use SignalFlow to analyze incoming data and write custom chart and detector analytics.

You can use the following SignalFlow components to create custom analytics for your data:

  • SignalFlow programming language: A Python-like language that you use to write SignalFlow programs.

  • SignalFlow library: Functions and methods you can call from a SignalFlow program.

  • SignalFlow computation engine: The engine that runs your SignalFlow programs in the background and streams results.

To learn more, see SignalFlow and analytics.

Virtual metrics 🔗

When you collect infrastructure data from different sources, infrastructure metrics for the same host can vary in naming conventions and value scale. For example, infrastructure metrics from AWS CloudWatch, Google Cloud Platform, Azure Monitor, and the Splunk Distribution of OpenTelemetry Collector might not all share the same naming conventions.

To make it easier for you to find and work with metrics coming in from different sources, Splunk Infrastructure Monitoring pulls data from different sources, transforms them, and returns them in a unified format called virtual metrics.

Example of virtual metrics: ^aws.ec2.cpu.utilization.

For more information, see Virtual metrics in Splunk Infrastructure Monitoring.