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.
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.
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:
A system architecture showing Azure resources forwarding telemetry via diagnostic settings to a Log Analytics workspace, where Kusto queries drive dashboards and alert rules that trigger Action Groups with webhooks into Azure DevOps or GitHub Actions for automated incident response.
Azure Backup Metric Alerts monitor the health of backup items using custom rules in Azure Monitor metrics. They generate alerts for job successes or failures, helping maintain system integrity. You can also migrate from classic alerts to Azure Monitor-based alerts, which offer more consistency and broader support across Azure services. The newer alert model uses the same Log Analytics workspace and Action Groups, so your team has a single alert interface.
Azure Monitor integrates with Azure AD B2C logs for extensive monitoring of user sign-in and sign-up events. By routing these logs to a Log Analytics workspace, you gain long-term log retention and support compliance and security audits. You can also send the data to an Azure storage account or Event Hubs for further analysis by SIEM tools. This setup works across multiple Azure AD B2C tenants by routing all logs to the same Log Analytics workspace, simplifying cross-tenant monitoring.
When designing your telemetry pipeline, adjust data retention periods in Log Analytics based on your organization’s needs. Longer retention supports detailed historical analysis but increases cost. Use built-in and custom reports to analyze trends, job performance, and identify optimization opportunities. For programmatic alert management, Azure Monitor’s REST APIs let you create and update alerts across different ecosystems, ensuring consistent alert mechanisms. This end-to-end pipeline enhances observability and responsiveness within your CI/CD environments, enabling proactive management and faster resolution of issues.
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Configuring diagnostic settings for an Azure resource defines which metrics and logs to collect and where to send them, typically to a Log Analytics workspace, to establish a telemetry pipeline for monitoring and analysis.
An Azure Monitor alert uses an Action Group that can trigger a webhook to call Azure DevOps or GitHub Actions, starting an automated incident response such as creating a work item or running a pipeline.
Kusto queries extract, filter, and summarize telemetry data stored in Log Analytics to create the foundation for dashboards and workbooks, providing real-time insight into metrics like backup success rates.
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