惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Google DeepMind News
Google DeepMind News
S
Security Affairs
阮一峰的网络日志
阮一峰的网络日志
L
LangChain Blog
Microsoft Azure Blog
Microsoft Azure Blog
雷峰网
雷峰网
Recent Announcements
Recent Announcements
WordPress大学
WordPress大学
The GitHub Blog
The GitHub Blog
博客园_首页
The Cloudflare Blog
M
MIT News - Artificial intelligence
博客园 - 【当耐特】
MyScale Blog
MyScale Blog
S
SegmentFault 最新的问题
P
Proofpoint News Feed
Y
Y Combinator Blog
Jina AI
Jina AI
博客园 - 聂微东
A
About on SuperTechFans
Blog — PlanetScale
Blog — PlanetScale
博客园 - 司徒正美
G
Google Developers Blog
云风的 BLOG
云风的 BLOG
F
Full Disclosure
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Microsoft Security Blog
Microsoft Security Blog
爱范儿
爱范儿
T
Tailwind CSS Blog
J
Java Code Geeks
Vercel News
Vercel News
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Stack Overflow Blog
Stack Overflow Blog
罗磊的独立博客
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
T
The Blog of Author Tim Ferriss
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
博客园 - 三生石上(FineUI控件)
W
WeLiveSecurity
PCI Perspectives
PCI Perspectives
Attack and Defense Labs
Attack and Defense Labs
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
宝玉的分享
宝玉的分享
IT之家
IT之家
Hacker News: Ask HN
Hacker News: Ask HN
The Register - Security
The Register - Security
T
The Exploit Database - CXSecurity.com
T
Threat Research - Cisco Blogs

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
Streamline Azure container monitoring with the Datadog AKS cluster extension
Addie Beach, Michael Cronk · 2024-01-24 · via Datadog | The Monitor blog
Addie Beach

Addie Beach

Technical Content Writer

Michael Cronk

Michael Cronk

Azure Kubernetes Service (AKS) enables you to easily deploy and manage containerized applications in Azure while leveraging Microsoft resources such as development tools, security features, and more. As with any Kubernetes service, the sheer volume of containers being orchestrated makes monitoring AKS cluster health challenging, which can slow response times to critical incidents and create bottlenecks around long-term optimizations.

Datadog’s AKS integration already provides complete visibility into your AKS clusters—once you’ve enabled the integration and deployed the Datadog Agent to your clusters, Datadog automatically begins collecting metrics and logs from your entire AKS setup and organizing them into high-level visualizations. However, the fact that many teams use third-party services such as Helm and Ansible to install the Datadog Agent on their clusters can add complexity to workflows and increase overhead. With the Datadog cluster extension for AKS, you can now easily deploy the Datadog Agent to your Kubernetes clusters directly within Azure—no other tools needed.

In this post, we’ll explore how you can:

  • Quickly deploy the Datadog Agent across your AKS clusters

  • Visualize AKS cluster and control plane activity

Quickly deploy the Datadog Agent across your AKS clusters

AKS cluster extensions make it easy to deploy services to your AKS clusters at scale and manage them from Azure Resource Manager. Like other cluster extensions, the Datadog AKS extension provides two methods of installation, enabling you to choose the deployment method that works best for your workflows. One way is to search for the Datadog AKS extension within Azure Marketplace. The extension setup page then enables you to configure details such as the relevant resource group, region, and cluster name.

The setup page for the Datadog AKS Cluster Extension, including options for setting the project and instance details.

Alternatively, you can also access the extension setup page directly from your Azure Kubernetes clusters by selecting the service you want to monitor, then choosing the Extensions and applications option from the sidebar.

Finally, before you can begin collecting AKS metrics in Datadog, you’ll want to enable the Azure integration. You can do so either from the Azure Portal via our Azure Native integration, or within Datadog by accessing the Azure integration tile.

Visualize AKS cluster and control plane activity

Once you’ve deployed the Datadog Agent to your clusters, metrics and logs from your AKS setup immediately begin streaming into Datadog. By using Datadog’s monitors and the OOTB AKS dashboard, you can quickly detect issues in your nodes before they bring processing to a halt.

The information Datadog ingests includes logs from the AKS control plane, which manages cluster resources. These logs contain critical information about the status of various orchestration components, including your API server, scheduler, and controller manager. With the AKS dashboard, you can view your control plane logs alongside performance metrics from the rest of your clusters, enabling you to quickly trace the root cause of issues no matter where they occur in your Kubernetes setup.

The OOTB AKS dashboard in Datadog, with metrics such as the cluster and node counts, average CPU utlization by node, and total number of unhealthy clusters displayed.

Let’s say that you receive an alert of a spike in CPU utilization across several nodes. While inspecting the dashboard, you notice an increase in clusters reporting an unhealthy status and, by scrolling down and viewing the logs for your control plane components, you also see an increase in error messages for the controller manager. The issues with your controller manager have led to fewer pods being created, causing the CPU on your existing nodes to overload. From here, you can take steps to debug the problematic node and prevent future issues, such as switching to a high-availability cluster with multiple control plane nodes.

Start monitoring your AKS clusters in minutes

The Datadog AKS integration helps you catch issues across all your Azure clusters, but using third-party tools to install the Datadog Agent on your services can lead to increased overhead and tool sprawl. With the Datadog AKS cluster extension, you can easily deploy the Datadog Agent to your clusters directly within Azure. Once the extension is enabled and the Agent is installed, metrics and logs from your AKS setup start streaming to the AKS dashboard in Datadog, so you can immediately begin analyzing troubleshooting data.

You can start monitoring your AKS clusters within Datadog by using the AKS integration—see our docs for more information. Or, if you’re new to Datadog, you can sign up for a 14-day free trial.