Setting Up Telemetry Pipelines
To establish a telemetry pipeline, you configure diagnostic settings and data sources to forward resource metrics and logs into Log Analytics. These settings send data such as backup job success metrics to a Log Analytics workspace, where the data can be analyzed. This creates a pipeline that supports continuous integration and continuous deployment (CI/CD) workflows, enabling efficient monitoring and automated incident response.
Configuring Diagnostic Settings and Data Sources
The first step is to configure diagnostic settings for each Azure resource you want to monitor. Diagnostic settings define which metrics and logs to collect and where to send them—typically to a Log Analytics workspace. You also choose which data sources (for example, performance counters, event logs, or custom logs) to stream. Once the data arrives in Log Analytics, you can run queries and build visualizations.
Authoring Kusto Queries for Dashboards and Workbooks
With telemetry data stored in Log Analytics, you use Kusto queries to extract, filter, and summarize the information. These queries become the foundation for Azure dashboards and workbooks. Dashboards give a quick overview of key metrics, while workbooks allow you to combine multiple queries and visualizations into a single interactive report. For example, you can create a workbook that shows backup success rates over time and lists recent failures, giving your team real-time insight without needing to open multiple screens.
When a metric or log condition is met, an alert fires. To make alerts actionable, you use Action Groups—collections of notification and action preferences. An Action Group can send an email or SMS, but more importantly it can trigger a webhook that calls Azure DevOps or GitHub Actions. This integration starts an automated incident response, such as creating a work item, running a pipeline to roll back a release, or notifying a chat channel. The key steps for this integration are:
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