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Datadog | The Monitor blog

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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Monitor your Windows containers with Datadog
Mary Jac Heuman · 2020-07-31 · via Datadog | The Monitor blog
Mary Jac Heuman

Mary Jac Heuman

As cloud providers and infrastructure technologies grow their support for Windows containers, developers who use the Windows ecosystem are more and more able to enjoy the benefits of containerization. It’s quicker and easier than ever to modernize and deploy applications that use Windows-specific frameworks like .NET. Plus, Windows developers can use orchestration services like Kubernetes, Amazon ECS, or Docker Swarm to manage the complexity that containerized environments introduce.

While teams move their Windows services to containers, it’s important to continuously monitor application activity to ensure it’s behaving as expected and make informed decisions on how to scale. Datadog provides deep visibility into your Windows containers. Whether that’s Kubernetes, Docker, or a cloud-provider service such as Google Kubernetes Engine, Azure Kubernetes Service, or Amazon Elastic Kubernetes Service, you can get full insight into whatever environment you are running.

Out-of-the-box Kubernetes dashboard

To get the Datadog Agent up and running, visit our documentation for setup instructions for your specific environment. Once you’ve installed the Agent on your cluster, Datadog automatically pulls real-time data from each host and the containers running on it in order to populate customizable, out-of-the-box dashboards. This means you can track at a glance resource usage, node state, and other metrics from your environment and containerized applications at a glance.

Monitor your Windows containers in real time

Containerized environments are inherently dynamic, so getting real-time visibility is crucial to responding to problems quickly. Datadog’s Container Map gives you a high-level overview of your entire fleet as containers are created and scaled.

Container map view

For more granular insights, the Live Container view provides easy access to resource metrics like CPU capacity and memory usage across all of your containers, updating every three seconds. Datadog automatically tags each container with key metadata, such as region, container image, cluster, and more. This means you can easily filter and drill down to the exact containers you want to see in order to investigate issues. After selecting a container, you can seamlessly view associated logs and traces for more context as you troubleshoot.

Live container list view

Container orchestration technologies like Kubernetes or Amazon ECS help you manage containerized environments. But, as they create and destroy containers on your behalf, it can be difficult to track what’s running where. The Datadog Agent’s Autodiscovery feature solves this problem for you by automatically detecting services running on containers as they are created. It then applies the appropriate configuration and collects relevant metrics. Tags mean you can easily search and organize your dynamic fleet and get a complete picture of your infrastructure’s health and performance even as containers churn.

Collect logs and traces for deeper visibility

For even more insights into your containerized environment, the Datadog Agent can also collect logs and distributed request traces from the services running on your Windows containers. You can easily pivot from high-level infrastructure metrics to associated logs or traces for total context, making it easy to find the source of errors or resource bottlenecks and determine the impact on your containers’ vital signs.

For instance, if a dashboard shows that a service is reporting high latency, you can click through the offending metric to view the traces and logs corresponding to that point in time. You can drill down into a single trace by viewing a flame graph that visualizes the lifespan of the request, which is in turn broken down into spans revealing sources of latency and errors. In the same pane, you can see all logs and underlying infrastructure metrics for complete visibility.

Example of a flame graph for a request made to a .NET application

Start monitoring Your Windows Containers

Datadog provides visibility into your containerized Windows-based applications across whichever environment you are using, from a bird’s-eye view of your entire infrastructure to the minute details of a single request’s lifespan. And, with more than 1,000 integrations, you can monitor the health and performance of your entire stack as you migrate to a modern, cloud-native environment. Get started by visiting our documentation for installation instructions, or sign up here for a free 14-day trial.