1. Proactive Anomaly Detection with Azure Alerts


    Proactive anomaly detection in Azure involves creating alert rules that automatically trigger and notify you when unusual behavior is detected in your cloud resources. It can help you stay ahead of potential issues by monitoring the metrics and logs and comparing them against historical patterns to identify anomalies.

    In the context of Azure, you would typically utilize Azure Monitor's Alert Rules along with Proactive Detection Configuration to set up an automatic anomaly detection mechanism. Here's how you would set this up:

    1. AlertRule: This resource is a part of Azure Monitor and allows you to define when an alert should be triggered. You will specify conditions based on metrics or logs coming from your resources. When these conditions are met, such as an unusual spike in traffic or failure rates, an alert is triggered.

    2. ProactiveDetectionConfiguration: This resource allows you to configure custom alerts for proactive detection on Azure App Services. It automatically analyzes the telemetry sent by App Services and generates alerts on detected anomalies.

    3. ActionGroup: When an alert is triggered, you need to specify what action should be taken. The ActionGroup resource defines a collection of actions to perform when an alert is triggered. Commonly, this involves sending an email notification or triggering a webhook to integrate with other services.

    Here's how you would write a Pulumi program in Python to create an anomaly detection alert with Azure:

    import pulumi import pulumi_azure_native.insights as insights import pulumi_azure_native.alertsmanagement as alertsmanagement # Proactive anomaly detection configuration should be linked with your App Service (or another supported resource). # Replace `app_service_plan_name` and `resource_group_name` with your own values. proactive_detection_config = insights.ProactiveDetectionConfiguration( "anomalyDetectionConfig", custom_emails=["alerts@example.com"], # List of custom emails for notifications enabled=True, # Enable the proactive detection name="anomalyDetection", # Configuration name resource_group_name="myResourceGroup", # Azure Resource Group for the App Service send_emails_to_subscription_owners=False, # Do not send emails to subscription owners # Additional parameters... ) # Define an action group to specify actions to take when an alert is triggered. action_group = alertsmanagement.ActionRuleByName( "anomalyActionGroup", resource_group_name="myResourceGroup", action_rule_name="anomalyActionGroupName", location="Global", # Since it's a global action group, we specify "Global" for the location properties=alertsmanagement.ActionRulePropertiesArgs( status="Enabled", rule_type="Suppression", # This example uses Suppression, however, you could use an “ActionGroup” for active alerts conditions=alertsmanagement.ConditionsTypeArgs( severity=alertsmanagement.ConditionOperatorArgs( operator="Equals", values=["Sev0", "Sev1"], # Define which severities to include ), # Additional conditions... ), # Additional properties... ), # Additional parameters... ) # Export URLs or other important information if necessary pulumi.export('proactive_detection_config_id', proactive_detection_config.id) pulumi.export('action_group_id', action_group.id)

    Make sure you replace "myResourceGroup" with the actual name of your resource group and customize the emails, name, and conditions to suit your requirements.

    In this program, we define a proactive detection configuration for an imaginary App Service and an action group necessary to send alerts. When the anomaly detection system spots unusual activity based on the conditions you set, it will trigger the action group to notify you.

    Please note that you'll need to have the appropriate Pulumi configuration set up, including authentication and Azure subscription details, to run this code successfully. This is generally accomplished through the Azure CLI or by setting up environmental variables for Pulumi to use.