Azure Monitor and Databricks Integration

Input and output integration overview

Gather metrics from Azure resources using the Azure Monitor API.

Use Telegraf’s HTTP output plugin to push metrics straight into a Databricks Lakehouse by calling the SQL Statement Execution API with a JSON-wrapped INSERT or volume PUT command.

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.

Databricks

This configuration turns Telegraf into a lightweight ingestion agent for the Databricks Lakehouse. It leverages the Databricks SQL Statement Execution API 2.0, which accepts authenticated POST requests containing a JSON payload with a statement field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a PUT ... INTO /Volumes/... command) that lands the metrics into a Unity Catalog table or volume governed by Lakehouse security. Under the hood Databricks stores successful inserts as Delta Lake transactions, enabling ACID guarantees, time-travel, and scalable analytics. Operators can point the warehouse_id at any serverless or classic SQL warehouse, and all authentication is handled with a PAT or service-principal token—no agents or JDBC drivers required. Because Telegraf’s HTTP output supports custom headers, batching, TLS, and proxy settings, the same pattern scales from edge IoT gateways to container sidecars, consolidating infrastructure telemetry, application logs, or business KPIs directly into the Lakehouse for BI, ML, and Lakehouse Monitoring. Unity Catalog volumes provide a governed staging layer when file uploads and COPY INTO are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.

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>

Databricks

[[outputs.http]]
  ## Databricks SQL Statement Execution API endpoint
  url = "https://{{ env "DATABRICKS_HOST" }}/api/2.0/sql/statements"

  ## Use POST to submit each Telegraf batch as a SQL request
  method = "POST"

  ## Personal-access token (PAT) for workspace or service principal
  headers = { Authorization = "Bearer {{ env "DATABRICKS_TOKEN" }}" }

  ## Send JSON that wraps the metrics batch in a SQL INSERT (or PUT into a Volume)
  content_type = "application/json"

  ## Serialize metrics as JSON so they can be embedded in the SQL statement
  data_format = "json"
  json_timestamp_units = "1ms"

  ## Build the request body.  Telegraf replaces the template variables at runtime.
  ## Example inserts a row per metric into a Unity-Catalog table.
  body_template = """
  {
    \"statement\": \"INSERT INTO ${TARGET_TABLE} VALUES {{range .Metrics}}(from_unixtime({{.timestamp}}/1000), {{.fields.usage}}, '{{.tags.host}}'){{end}}\",
    \"warehouse_id\": \"${WAREHOUSE_ID}\"
  }
  """

  ## Optional: add batching limits or TLS settings
  # batch_size = 500
  # timeout     = "10s"

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.

Databricks

1. Edge-to-Lakehouse Telemetry Pipe: Deploy Telegraf on factory PLCs to sample vibration metrics and post them every second to a serverless SQL warehouse. Delta tables power PowerBI dashboards that alert engineers when thresholds drift.
2. Blue-Green CI/CD Rollout Metrics: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by deployment_id, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions.
3. SaaS Usage Metering: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.
4. Security Forensics Lake: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using PUT commands, then run COPY INTO for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.

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