You use Azure monitor every time you create an Azure resource. Azure Monitor starts collecting data each time you log into Azure. You see Azure Monitor data every time you look at the overview page of an Azure resource, like a virtual machine in the portal. Azure Monitor collects CRUD (create, read, update, and delete) type data from users, performance metrics, historical events, and log data from resources and applications. As soon as you create a resource - actually as soon as you create a subscription - activity logs start recording administration interactions, like creation and modification. Two basic types of data are saved.

• Metrics – lightweight numerical data often available in near real-time describing various aspects of a systems or resources state at a point in time. Deviation of metrics data from a nominal state is ideal for quickly detecting resource or application issues and raising alerts.
• Logs – in-depth telemetry data usually related to events and actions occurring in resources, where records contain multiple attributes.

The recorded data comes from several sources

• Application monitoring data – Platform-agnostic performance and operational data for your custom applications.
• Guest OS data – Monitoring data for operating systems running either on-premises or on infrastructure you've deployed to Azure.
• Resource monitoring – Performance and operational data-related resources deployed to Azure, including activity log for service health and configuration changes.
• Subscription and tenant monitoring - Data related to the operation of your subscription like sign-on information from Azure Active Directory audit logs and the health of Azure in general.
• Custom sources – Azure monitor has APIs that will enable you to ingest data from any REST API client.

A resource's metrics can be viewed through the monitoring and metrics functions within a resource or via the Azure Monitor service. Metrics Explorer enables you to view a resource's metrics data interactively or combine metrics data from multiple applications and resources to see how they're working, or not, in unison. 

Log data can be analyzed with a variant of the Kusto query language, a non-procedural data interrogation language, a bit like SQL, although the syntax is quite different. Results from these queries can be further analyzed and visualized in various ways with dashboards and reporting tools like Power BI. You can use workbooks to combine metrics and log data with text and parameters to create interactive reporting.

Deeper insights can be gained through automated artificial intelligence analysis that provides suggestions and advice on improving resources performance and efficiency for most common Azure resource types. Application insights look at performance, usage, and exception data providing in-depth analysis of all components that make up an application. Network Insights monitors the health and performance of all your deployed network resources.

As well as historical analysis, you can set up rules that trigger specific actions like alerts or resource scaling to meet increased demand. The rule can be simple, as when a metric value exceeds a static threshold. Dynamic rules use artificial intelligence analysis to determine how much a metric value has deviated from the norm. You can set a rule's level of sensitivity to the deviation to specify when to take action. Rules can be based on metric data or the result of a log query, and in addition to alerts and auto-scaling, you can launch an Azure logic App, an Azure function, or Azure Runbook as well as calling a webhook.

Log and metric data can be streamed to Azure Event Hubs to make it accessible to third-party tools, either within or external to Azure.