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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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Monitor OpenTelemetry-native metrics with Datadog
2025-06-03 · via Datadog | The Monitor blog

OpenTelemetry (OTel) is emerging as the industry standard for collecting and transmitting observability data. Datadog supports several ways to send and accept OTel-native data, while also continuing to support its own native telemetry format. To provide a consistent monitoring experience, Datadog now supports using OTel-native metrics alongside Datadog-native metrics across dashboards, queries, and core visualizations in the Datadog platform. This results in a unified observability experience whether you’re sending data via OpenTelemetry, the Datadog Agent, or a combination of both, allowing teams the flexibility to adopt OTel at their own pace without disrupting their existing monitoring workflows.

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

View OTel-native metrics in out-of-the-box integration dashboards

Teams using OTel-native telemetry for integrations like Kafka, Docker, NGINX, and more can now view their data in Datadog’s out-of-the-box (OOTB) dashboards, providing the same onboarding experience as with telemetry data collected by the Datadog Agent.

The OOTB integration dashboards offer immediate visibility into system health and performance with minimal setup. They highlight the most important metrics to monitor, giving you a quick start to troubleshooting as soon as an integration is enabled. Now, with support for OTel-native formats and semantics, these dashboards make it easy for OTel-native users to use the same integration dashboards with no additional configuration or manual metrics mapping required.

For example, the “Kafka, Zookeeper and Kafka Consumer Overview” dashboard is now compatible with metrics sent from both the Datadog Agent-based Kafka integration and OTel-based sources, including kafkametricsreceiver. This makes it easier for SREs to monitor critical Kafka metrics, regardless of how Kafka telemetry is collected. With unified metric semantics, you can use a single dashboard to detect issues like rising consumer lag without needing to maintain separate queries or manually normalize metrics.

Kafka dashboard with OTel metrics.

See our documentation for configuring OTel Kafka metrics. Datadog also supports dashboard compatibility for metrics sent from integrations such as Docker, MySQL, and NGINX receivers, and continues to add support for other integrations.

Query across Datadog and OTel metric semantics

Datadog now also supports OTel-native data outside of OOTB dashboards, enabling customers with OTel and Datadog metrics in their deployments to query across both data sources with one query using the Semantic Mode selector. Within any metrics query editor in the Datadog platform, including in Metrics Explorer, dashboards, and monitors, you can easily adjust queries to “Combine data from all telemetry sources”, including OpenTelemetry.

Setting the Semantic Mode to “Combine data from all telemetry sources” automatically detects and includes equivalent metrics collected via both OTel-native and Datadog-native tooling, ensuring your queries are comprehensive and accurate with minimal configuration. Rather than renaming metrics or enforcing a single naming scheme, Datadog relates semantically equivalent metrics behind the scenes. This means you can write a single query to include relevant data from both formats without needing to manually map names or rewrite your queries, allowing you to save time and focus on more meaningful monitoring and analysis.

Semantic Mode selector for OTel or Datadog-native data.

For example, both OTel and Datadog have a metric to measure the total number of bytes sent out by a Kafka producer to the Kafka brokers. However, they have different names:

  • OTel-native metric: kafka.producer.outgoing_byte_rate
  • Datadog Agent metric: kafka.producer.bytes_out

Selecting the Semantic Mode to combine data from all telemetry sources shows the aggregated results of both metrics across all instances of Kafka producers. Continuing the example, the following query shows the amount of data transferred from all Kafka producer hosts whether they are reporting OTel- or Datadog-native metric data.

Visualizing a Kafka metric with OTel and Datadog data.

To understand the relationship between equivalent Datadog and OTel metrics, visit the Metrics Details page of each metric within the Metrics Explorer. Here you’ll find the details of a metric and its equivalences.

Metric details of a Kafka metric showing OTel and Datadog support.

Visualize your Datadog and OTel data together

With OOTB dashboard support for OTel-native data, users can easily monitor OTel-native telemetry using the same powerful dashboards available for Datadog-native data. Paired with the new Semantic Mode selector (available in Preview), users with mixed OTel and Datadog environments can write queries to aggregate, process, and visualize telemetry from both telemetry sources without any manual mapping or complex configuration. These two new capabilities enable faster insights, reduce overhead, and provide a unified observability experience across OpenTelemetry and Datadog.

For more on Datadog’s compatibility with OpenTelemetry, read our blog post on the Datadog Distribution of the OpenTelemetry Collector (DDOT), which provides an enterprise-ready, OTel-native Collector distribution that enables users to adopt OTel flexibility while taking full advantage of Datadog’s advanced monitoring and security features. And if you’re new to Datadog, get started with a 14-day free trial.