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

小众软件
小众软件
IT之家
IT之家
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
Security Archives - TechRepublic
Security Archives - TechRepublic
P
Proofpoint News Feed
C
CERT Recently Published Vulnerability Notes
阮一峰的网络日志
阮一峰的网络日志
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
The Cloudflare Blog
P
Palo Alto Networks Blog
Know Your Adversary
Know Your Adversary
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Cisco Talos Blog
Cisco Talos Blog
L
Lohrmann on Cybersecurity
AWS News Blog
AWS News Blog
J
Java Code Geeks
博客园_首页
Scott Helme
Scott Helme
WordPress大学
WordPress大学
有赞技术团队
有赞技术团队
T
The Exploit Database - CXSecurity.com
Security Latest
Security Latest
V
Visual Studio Blog
Cloudbric
Cloudbric
Jina AI
Jina AI
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
博客园 - 叶小钗
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 聂微东
人人都是产品经理
人人都是产品经理
A
Arctic Wolf
C
Cybersecurity and Infrastructure Security Agency CISA
S
SegmentFault 最新的问题
The Last Watchdog
The Last Watchdog
SecWiki News
SecWiki News
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
W
WeLiveSecurity
K
Kaspersky official blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Hacker News: Ask HN
Hacker News: Ask HN
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
宝玉的分享
宝玉的分享
Hugging Face - Blog
Hugging Face - Blog
量子位
Google Online Security Blog
Google Online Security Blog
博客园 - Franky
Simon Willison's Weblog
Simon Willison's Weblog
博客园 - 三生石上(FineUI控件)
Recent Commits to openclaw:main
Recent Commits to openclaw:main

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
Automate Agent installation with the Datadog Ansible collection
Bowen Chen, Lutao Xie · 2023-09-21 · via Datadog | The Monitor blog
Bowen Chen

Bowen Chen

Lutao Xie

Lutao Xie

Ansible is a configuration management tool that helps you automatically deploy, manage, and configure software on your hosts. By turning manual workflows into automated processes, you can quicken your deployment lifecycle and ensure that all hosts are equipped with the proper configurations and tools.

The Datadog collection is now available in both Ansible Galaxy and Ansible Automation Hub. After installing the collection, you’ll gain access to all of the latest features and tools we’ve developed to enhance your automation workflow. This includes the Datadog Ansible role, which can be used to automatically install and configure the Datadog Agent and its integrations across your infrastructure.

In this post, we’ll provide a brief introduction to how Ansible works and the benefits of the Datadog collection. We’ll also walk through an example of using Datadog’s Ansible collection to install the Agent onto managed hosts.

How Ansible works

Ansible is installed onto a single host (known as the control node) used to remotely manage your target group of managed hosts via SSH and other transport protocols. This automation tooling is split into two major components: inventories and playbooks. Inventories are groups of managed hosts that Ansible will deploy and configure. On the other hand, playbooks are automation blueprints for tasks that can be used to deploy and configure any inventory.

You likely won’t need to develop all of your playbook content from scratch—there is plenty of open source content developed by Ansible partners and the community to help you jump-start your automation. Blueprints for tasks such as deploying the Datadog Agent can be installed as Ansible roles, which you can then apply within your playbook using a single line of code. You can install roles and other related playbooks, modules, and plugins as part of a collection, Ansible’s distribution format that groups content from a single creator.

You can browse a comprehensive list of collections within Ansible Galaxy, an open source repository for all community-developed content, or Ansible Automation Hub, which provides certified content for enterprise customers.

Install our certified collection from Ansible Automation Hub to begin enhancing your workflows.

By installing Datadog’s Ansible collection, you’ll gain access to new automation content such as modules, plug-ins, and roles as soon as we ship them. Our collection is Red-Hat certified—this means everything we release will be ready for use in your production environments and fully supported by both Red Hat and Datadog. And if you install Datadog’s certified collection through the Ansible Automation Hub, you can reach out to Red Hat’s support team if you need any help. We’ll continue to release new updates to the role and maintain functional parity with the collection for an extended period of time to help support an easy migration to the collection. We also plan to release enhanced workflows for customers who choose to install the collection. As we release new content, you’ll be able to implement additional features directly in your playbooks and environments that allow you to manage and configure the Datadog Agent more efficiently.

Installing the Agent using the Datadog collection

In this section, we’ll walk through how to use Datadog’s Ansible collection to automatically deploy the Agent. To get started, make sure you’re running an up-to-date version of Ansible on your control node. You’ll then need to install the datadog.dd collection using Automation Hub or using the following Ansible Galaxy CLI script:

ansible-galaxy collection install datadog.dd

After you’ve installed the collection, you’ll need to create an inventory that specifies the hosts you want to monitor with Datadog. In the example below, we’ve created a simple inventory (inventory.yaml) using a mix of host names and IP addresses. Grouping your hosts by web, databases, and prod enable you to create and run playbooks that apply only to hosts in these subfields.

[web]

203.0.113.112

203.0.113.113

[databases]

us-west-db1

us-east-db1

[prod]

203.0.113.112

prod-us1

You can also create a dynamic inventory by using cloud provider tags to define the scope of your inventory; this ensures that the inventory will automatically update as new hosts are created. If you use AWS, you can check out this blog post to learn how to install Datadog on your EC2 hosts by creating a dynamic inventory.

Now that we have an inventory, it’s time to create our playbook (datadog_playbook.yaml) that installs the Agent on our inventory of hosts. We accomplish this by importing the Datadog role as shown below. In our example, we provide the Datadog API key as a string—however, we recommend encrypting your credentials with a solution such as Ansible Vault.

- name: Install the Datadog Agent

import_role:

name: datadog.dd.agent

vars:

datadog_api_key: "<YOUR_API_KEY>"

We can now apply our playbook to our inventory. To install the Datadog Agent on all hosts within our inventory.yaml file, we can run the following command on our control node:

ansible-playbook -i inventory.yaml datadog_playbook.yaml

You can also easily configure your playbook to install Datadog integrations so you can gain full visibility across your environment. To learn more, check out our documentation for the agent role.

Start using Datadog’s Ansible collection

Datadog’s Ansible collection is now certified through Ansible Automation Hub and also available via Ansible Galaxy. Begin automating the installation and configuration of the Agent on your hosts today and gain access to new and advanced features that help you manage your infrastructure more efficiently. You can also follow these in-app instructions to configure the Agent. To learn more about using Datadog with Ansible, you can check out our documentation. Or, check out these blog posts on deploying the Agent on Windows environments using Ansible and installing the Agent on AWS hosts using Ansible dynamic inventories.

If you don’t already have a Datadog account, you can sign up for a free 14-day trial today.