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Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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New integrations for Azure monitoring
Daniel Langer · 2018-06-15 · via Datadog | The Monitor blog
Daniel Langer

Daniel Langer

We are proud to announce that we have enhanced our Microsoft Azure integration to support more than 60 Azure services, including Cosmos DB, Service Bus, and Azure DB for MySQL and PostgreSQL. Datadog now automatically collects metrics and tags from all services supported by Azure Monitor to provide comprehensive Azure monitoring through one integration.

We’re also excited to announce that we have officially released support for Azure Service Fabric, a distributed systems platform that makes it easy to package, deploy, and manage reliable, auto-scaling microservices and containers. Datadog’s Azure Virtual Machine extension enables you to deploy the Datadog Agent directly on the auto-scaling nodes in any Service Fabric cluster, so you can track their health and resource consumption in real time.

Datadog integrates with more than 60 Azure services, so you can create custom dashboards that help you visualize your dynamic environment in one place.
Azure monitoring with Datadog - overview dashboard
Datadog integrates with more than 60 Azure services, so you can create custom dashboards that help you visualize your dynamic environment in one place.

These enhancements build on our existing support for services like Azure Classic and ARM Virtual Machines, Azure SQL Database, and Azure App Services. Furthermore, we provide visibility into the broader Microsoft and Windows ecosystem with integrations such as Microsoft IIS and Windows Management Instrumentation.

Better tagging for Azure monitoring

Azure Monitor’s multi-dimensional metrics enable you to track timeseries values across one or more dimensions, or attributes of your data. Datadog’s enhanced Azure integration pulls in those dimensions as tags, so that you can slice, group, and filter your data however you’d like.

The dimensions provided by Azure vary from metric to metric (e.g., an IP address dimension for network traffic metrics or a disk-identifier dimension for disk space metrics). The screenshot below indicates that Azure Monitor provides a Transactions metric that tracks the number of requests to Blob Storage accounts across three dimensions: ResponseType, GeoType, and ApiName.

Azure monitoring with multi-dimensional metrics that can be collected by Datadog

Datadog automatically converts dimensions into key:value format (e.g., responsetype:success) and tags each metric accordingly. You can use these tags in dashboards and alerts to get deeper insights into your Azure infrastructure. The example below shows how you can graph the average number of transactions hitting your Blob Storage accounts, broken down by API name and response type. You can explore this visualization to determine, for example, which types of requests generated successful responses or resulted in a higher rate of errors. These new tags make it easier than ever to troubleshoot issues in your Azure environment and quickly drill down to the data that matters to you.

Azure monitoring - Graphing Azure metrics using Monitor Azure dimensions in Datadog

Azure also lets you add custom key:value tags to your resources via the REST API, the Azure CLI, PowerShell, or the Azure Portal.

Azure monitoring - Applying custom tags to Azure resources to be collected in Datadog

Datadog will automatically collect your custom tags and apply them to relevant metrics, so that you can filter and group your data to create useful graphs and alerts. In the example below, we are filtering by a custom tag, pipeline_id, to visualize a particular segment of network traffic on an Azure VM instance.

Azure monitoring - using multidimensional tags to filter metrics in Datadog

Integrate with Azure Service Fabric

Monitoring the health of your Azure Service Fabric cluster in Datadog is as easy as running one command in the Azure command line interface.

To run the command, you’ll need to make note of four things:

  • the operating system your cluster is running (Windows or Linux)

  • the Resource Group your cluster is housed in

  • the name of the Virtual Machine Scale Set (VMSS) that is managing the underlying nodes in the cluster

  • your Datadog API key, which you can find in your account here

Next, log in to the Azure CLI by running az login. Run this command to deploy the Datadog Agent on the nodes in your cluster (making sure to replace <DATADOG_AGENT_NAME> with either DatadogWindowsAgent or DatadogLinuxAgent, depending on your cluster’s operating system):

az vmss extension set --name <DATADOG_AGENT_NAME> --publisher Datadog.Agent --resource-group <RESOURCE_GROUP_NAME> --vmss-name <VMSS_NAME> --protected-settings "{'api_key':'<YOUR_API_KEY>'}"

Alternatively, you can add the Datadog Azure Virtual Machine extension directly to the ARM template of your Service Fabric cluster.

Azure monitoring - visualizing an Azure Service Fabric cluster with Datadog graphs

And that’s it! Within a few minutes, Azure Resource Manager will deploy the Datadog Agent on each node in your cluster, and the Agent will begin reporting health metrics in the Datadog UI. Additionally, if you have already installed the Datadog Azure integration, metadata and tags from these nodes will be applied automatically.

Start using Datadog’s Azure integration

If you’re already monitoring Azure with Datadog, you can immediately start using these enhancements to get more visibility into your environment. Otherwise, follow the instructions here to get started.

If you’re new to Datadog, you can try out these Azure monitoring features with a 14-day free trial .