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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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Monitor Amazon EKS Distro with Datadog
2020-12-01 · via Datadog | The Monitor blog

Kubernetes has become the de facto standard for container orchestration, making it a key piece in the containerization strategy of many users. Amazon EKS Distro (EKS-D) is a Kubernetes distribution from AWS that lets you run on-premise clusters using the same tested Kubernetes versions, security features, and tooling that power Amazon EKS. This means that EKS-D is compatible with the Kubernetes tools and processes you’re already using, making it easy to operate in-house Kubernetes clusters.

Datadog is proud to partner with AWS for the launch of EKS-D. Our integrations for Kubernetes and other key technologies common to Kubernetes environments, like containerd, Istio and CoreDNS, bring you deep visibility into EKS-D so you can monitor all of your clusters—whether self-hosted or in the cloud—within the Datadog platform.

Gain visibility into your EKS-D environment with Datadog.
The cluster map filters pods by cluster name, availability zone, and environment, and groups pods by namespace.
Gain visibility into your EKS-D environment with Datadog.

Build and operate on-premise Kubernetes clusters

A fully managed Kubernetes service like EKS on Fargate removes the overhead of managing infrastructure and keeping components up to date, but doesn’t give you the control over your configuration that you may need to maintain your security posture or meet compliance requirements. Building on-premise Kubernetes clusters on EKS-D gives you greater control over your infrastructure along with the convenience of a Kubernetes distribution that’s maintained and tested by Amazon for reliability and security.

Like EKS, EKS-D is certified as a conformant Kubernetes distribution, so you can seamlessly migrate applications from Kubernetes or EKS to EKS-D. It uses open source Kubernetes and includes security patches and components—such as etcd, kubectl, and kube-controller-manager—plus networking and storage plugins. EKS-D is the same distribution that EKS runs on, and it’s available as an open source project that you can use to create on-premise clusters.

Extend your Kubernetes visibility to EKS-D

To successfully manage your Kubernetes clusters, you need visibility into the health and performance of each layer of your environment, including the control plane The orchestration layer that exposes APIs and internal services for cluster management , data plane, worker nodes , and network, so you can troubleshoot issues quickly. But monitoring Kubernetes is difficult because its dynamic workload scheduling and ephemeral infrastructure make it hard to know exactly where your workloads are running. The Datadog Agent and integrations are designed to tackle the challenges of monitoring Kubernetes clusters, including those powered by EKS-D.

The Datadog Agent collects node-level metrics from the hosts where your pods A group of containers running in a Kubernetes cluster are deployed so you have insight into the resource usage of your workloads. You can also get telemetry from key Kubernetes components like the API server, the controller manager, the scheduler, and etcd, so you can monitor your cluster’s control plane in detail.

Datadog’s Kubernetes integration collects metrics, events, and logs at the level of your cluster and pods and helps you understand the performance of the containers where your applications are running. Because EKS-D is a Kubernetes distribution, you can begin monitoring the health of any of your EKS-D clusters as soon as you’ve set up the Kubernetes integration. Datadog will automatically populate out-of-the-box dashboards, like the one below showing control plane performance, so you can visualize cluster health immediately.

A dashboard graphs the performance of the Kubernetes API server, scheduler, and controller manager, plus etcd.

Once you’ve started monitoring EKS-D, you can use additional integrations to get visibility into your entire stack with Datadog. For example, use the Docker and containerd integrations to collect container-level metrics. Autodiscovery automatically identifies and configures integrations as your cluster launches new containers, making it easy to collect metrics, as well as logs and traces, so you can track the health and performance of your dynamic, containerized applications. And you can get even more visibility using related integrations like CoreDNS and Istio to better understand the performance of your EKS-D cluster.

Monitor Amazon EKS Distro with Datadog

Datadog now gives you full visibility into Amazon EKS Distro. You can monitor your on-premise Kubernetes clusters—including your control plane, worker nodes, and network performance—plus more than 1,000 other technologies, all in a single platform. If you’re not already using Datadog, sign up today for a free 14-day trial.