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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
The new Datadog Agent: Omnibus is your ticket out of dependency hell
Remi Hakim · 2014-08-22 · via Datadog | The Monitor blog

Making a self-installing agent that works everywhere with all its dependencies for over 20 different OS versions is hard, and we solved that problem with Omnibus packaging.

The Datadog Agent is written in Python and has so far relied on the host OS Python. One problem is that OS Distributions can ship very different versions of Python - from Python 2.4 on CentOS/RHEL 5 to Python 3 on the latest releases of Fedora. Another challenge is that the Datadog Agent often relies on third parties libraries to collect metrics and events from the apps it monitors out of the box, such as your favorite database driver library.

Dependencies, dependencies, dependencies

Before Agent 5.0, every time you wanted to install a new integration that required a dependency, e.g. the Postgres integration, you had to go through the following steps.

Instructions for the Datadog Postgres integration
Omnibus
Instructions for the Datadog Postgres integration

That process could have failed for a number of reasons:

  • The dependency in the official repositories is outdated and not compatible with the Agent.

  • The dependency the Agent needs conflicts with one that is already installed and used by other applications.

  • There is no precompiled version of the dependency and it needs to be installed from source.

These issues forced us to make sure the Agent code worked with as many different versions of the dependency as possible. So we had to write tests to check what version was currently installed on your host OS:

Over time, some of you have hit these frustrating issues and we wanted to make the installation process seamless in 100% of cases.

How did we solve this problem? With Chef Omnibus.

Enter Omnibus

Omnibus

Omnibus is a tool developed by the Chef (formerly Opscode) team in order to “easily create full-stack installers for your project across a variety of platforms.” They use it to create installers for the Chef client and server that installs everything the client and server need to run.

We did the same and designed a new continuous Agent build process that combines the power of Github, Jenkins, Vagrant and S3 to create self-contained installer packages.

The Datadog Agent build process

The build process that creates platform-specific Agent installers
Omnibus
The build process that creates platform-specific Agent installers

First, we wrote an Omnibus project definition that lives on our Jenkins server. Jenkins uses it to provision a set of VMs that mimics our customers’ platforms. Jenkins triggers all the Omnibus builds. A build compiles the dependencies needed by the Agent as defined in the project definition from the Datadog dd-agent-omnibus repository and in the software definitions in the omnibus-software repository. Finally, Omnibus calls upon FPM to produce native packages for the target systems. We deploy those Agent installer packages to our apt and yum repositories on S3 for everyone to download.

The download is a simple file and contains all the dependencies the Agent needs. Upon installation, the dependencies will be copied to the system and installed in an isolated directory: /opt/datadog-agent. This ensures there’s no conflict with existing dependencies.

Et voilà! It is now possible to enable Datadog’s Agent-based integrations without having to worry about installing dependencies. The Agent will run regardless of your OS’ version of Python and won’t interfere with applications that require the same dependencies but at a different version.

With the new Agent 5.0, you can add integrations and access new features more easily so you have the visibility you need over your entire infrastructure. To take advantage of all that Agent 5.0 has to offer, sign-up for a 14-day free trial and visit this blog post on how to install or upgrade to Agent 5.0.