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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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Introducing the Datadog Open Source Hub
Ara Pulido · 2023-09-27 · via Datadog | The Monitor blog
Ara Pulido

Ara Pulido

At Datadog, we have always been deeply involved with open source software—producing it, using it, and contributing to it. Our Agent, tracers, SDKs, and libraries have been open source from the beginning, giving our customers the flexibility to extend our tools for their own needs. The transparency of our open source components also allows them to fully audit the Datadog software that is running on their systems.

But our commitment to open source only starts there.

When it comes to using open source software, we embrace an “upstream-first” approach. This means that if our engineers find a bug or think of a beneficial new feature while using open source projects to support our infrastructure, they will work closely with the original developers to bring about helpful changes. Our engineers report and fix bugs, propose improvements, and actively participate in the community to help see these improvements through to completion.

Additionally, many projects initiated by our engineers, even those not directly tied to our products, are made open source and maintained with community support.

Announcing the Datadog Open Source Hub

With this spirit of open collaboration in mind, we are happy to announce the Datadog Open Source Hub. The Open Source Hub is a central, dedicated space where you can learn about the projects we maintain and regularly contribute to, the ecosystems we participate in, and other related initiatives we’re engaged in.

Through the Open Source Hub, you can explore open source projects started at Datadog, hear from their maintainers, and discover how to contribute. In addition, you will hear from Datadog contributors involved in third-party open source projects. This gives you a chance to learn about their experiences, how and when they started contributing, and the impact of their work.

A Showcase for Open Source Projects

Finally, as you might already be aware, we offer free Datadog accounts to selected open source projects. The Open Source Hub will showcase some of these projects and how they have been using Datadog to their advantage.

We invite you to explore the new Datadog Open Source Hub to learn more about our open source initiatives. While we are excited about the Hub’s launch, it’s just the beginning. Stay tuned for regular updates, more highlighted projects and contributors, and an expanding showcase of open source projects supported by Datadog observability.