Azure Monitor and Azure Data Explorer Integration

Input and output integration overview

Gather metrics from Azure resources using the Azure Monitor API.

The Azure Data Explorer plugin allows integration of metrics collection with Azure Data Explorer, enabling users to analyze and query their telemetry data efficiently. With this plugin, users can configure ingestion settings to suit their needs and leverage Azure’s powerful analytical capabilities.

Integration details

Azure Monitor

The Azure Monitor Telegraf plugin is specifically designed for gathering metrics from various Azure resources using the Azure Monitor API. Users must provide specific credentials such as client_id, client_secret, tenant_id, and subscription_id to authenticate and gain access to their Azure resources. Additionally, the plugin supports functionality to collect metrics from both individual resources and resource groups or subscriptions, allowing for flexible and scalable metric collection tailored to user needs. This plugin is ideal for organizations leveraging Azure cloud infrastructure, providing crucial insights into resource performance and utilization over time, facilitating proactive management and optimization of cloud resources.

Azure Data Explorer

The Azure Data Explorer plugin allows users to write metrics, logs, and time series data collected from various Telegraf input plugins into Azure Data Explorer, Azure Synapse, and Real-Time Analytics in Fabric. This integration serves as a bridge, allowing applications and services to monitor their performance metrics or logs efficiently. Azure Data Explorer is optimized for analytics over large volumes of diverse data types, making it an excellent choice for real-time analytics and monitoring solutions in cloud environments. The plugin empowers users to configure metrics ingestion based on their requirements, define table schemas dynamically, and set various ingestion methods while retaining flexibility regarding roles and permissions needed for database operations. This supports scalable and secure monitoring setups for modern applications that utilize cloud services.

Configuration

Azure Monitor

# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
  # can be found under Overview->Essentials in the Azure portal for your application/service
  subscription_id = "<>"
  # can be obtained by registering an application under Azure Active Directory
  client_id = "<>"
  # can be obtained by registering an application under Azure Active Directory.
  # If not specified Default Azure Credentials chain will be attempted:
  # - Environment credentials (AZURE_*)
  # - Workload Identity in Kubernetes cluster
  # - Managed Identity
  # - Azure CLI auth
  # - Developer Azure CLI auth
  client_secret = "<>"
  # can be found under Azure Active Directory->Properties
  tenant_id = "<>"
  # Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic.
  # cloud_option = "AzurePublic"

  # resource target #1 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    # can be found under Overview->Essentials->JSON View in the Azure portal for your application/service
    # must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx'
    # must be removed from the beginning of Resource ID property value)
    resource_id = "<>"
    # the metric names to collect
    # leave the array empty to use all metrics available to this resource
    metrics = [ "<>", "<>" ]
    # metrics aggregation type value to collect
    # can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum'
    # leave the array empty to collect all aggregation types values for each metric
    aggregations = [ "<>", "<>" ]

  # resource target #2 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    resource_id = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # resource group target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    # the resource group name
    resource_group = "<>"

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      # the resource type
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # resource group target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    resource_group = "<>"

    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # subscription target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # subscription target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]
</code></pre>

Azure Data Explorer

[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true

  ##  Ingestion method to use.
  ##  Available options are
  ##    - managed  --  streaming ingestion with fallback to batched ingestion or the "queued" method below
  ##    - queued   --  queue up metrics data and process sequentially
  # ingestion_type = "queued"

Input and output integration examples

Azure Monitor

1. Dynamic Resource Monitoring: Use the Azure Monitor plugin to dynamically gather metrics from Azure resources based on specific criteria like tags or resource types. Organizations can automate the process of loading and unloading resource metrics, enabling better performance tracking and optimization based on resource utilization patterns.

2. Multi-Cloud Monitoring Integration: Integrate metrics collected from Azure Monitor with other cloud providers using a centralized monitoring solution. This allows organizations to view and analyze performance data across multiple cloud deployments, providing a holistic overview of resource performance and costs, and streamlining operations.

3. Anomaly Detection and Alerting: Leverage the metrics gathered via the Azure Monitor plugin in conjunction with machine learning algorithms to detect anomalies in resource utilization. By establishing baseline performance metrics and automatically alerting on deviations, organizations can mitigate risks and address performance issues before they escalate.

4. Historical Performance Analysis: Use the collected Azure metrics to conduct historical analysis by feeding the data into a data warehousing solution. This enables organizations to track trends over time, allowing for detailed reporting and decision-making based on historical performance data.

Azure Data Explorer

1. Real-Time Monitoring Dashboard: By integrating metrics from various services into Azure Data Explorer using this plugin, organizations can build comprehensive dashboards that reflect real-time performance metrics. This allows teams to respond proactively to performance issues and optimize system health without delay.

2. Centralized Log Management: Utilize Azure Data Explorer to consolidate logs from multiple applications and services. By utilizing the plugin, organizations can streamline their log analysis processes, making it easier to search, filter, and derive insights from historical data accumulated over time.

3. Data-Driven Alerting Systems: Enhance monitoring capabilities by configuring alerts based on metrics sent via this plugin. Organizations can set thresholds and automate incident responses, significantly reducing downtime and improving the reliability of critical operations.

4. Machine Learning Model Training: By leveraging the data sent to Azure Data Explorer, organizations can perform large-scale analytics and prepare the data for feeding into machine learning models. This plugin enables the structuring of data that can subsequently be used for predictive analytics, leading to enhanced decision-making capabilities.

Feedback

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