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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 your CircleCI environment with Datadog
2021-10-22 · via Datadog | The Monitor blog

Datadog CI Visibility provides a unified platform for monitoring your CI/CD pipelines. Now, we are partnering with CircleCI to extend that same critical visibility to your CircleCI environment. Datadog’s integration uses CircleCI webhooks to capture information about the status and performance of your workflows and associated jobs, such as a job’s duration and whether or not it failed or was canceled. Once you set up the integration, you will be able to:

Having this visibility into your CircleCI environment enables you to track performance trends in CI pipelines and surface problems that could affect a release, such as a job within a key workflow that is suddenly failing more often than others.

Ensure your CircleCI pipelines are performing optimally

Your CI/CD pipelines are key to releasing new features and bug fixes to customers, and a slow pipeline can prevent you from releasing on time. Datadog CI Visibility helps you track the status of your CircleCI workflows (referred to as pipelines in Datadog) and their constituent jobs via a built-in pipelines dashboard. This gives you valuable information about their overall performance, enables you to quickly spot build errors, and find areas that need optimization. For example, you can use the pipelines dashboard to easily compare performance across all running workflows and jobs in order to see which ones took the longest amount of time to execute.

CI Visibility pipelines dashboard for CircleCI

From there, you can decide the best course of action for improving pipeline efficiency, such as implementing a caching strategy for dependencies or configuring your tests (e.g., unit tests) to run in parallel.

Get more context for build failures

Pipeline failures are commonplace in CI environments, so it can be difficult to know when a failure is legitimate or not. Datadog CI Visibility makes it easy to see which jobs in your CircleCI environment are failing the most, so you can prioritize improving the reliability of critical jobs in order to avoid the delay of future releases.

CI Visibility pipeline failures for CircleCI

You can drill down to a specific pipeline execution to get more information, including a flame graph breakdown of every job and test that ran as part of that execution and their duration. Failed jobs are automatically highlighted in red, so you can quickly identify which ones you need to review first.

Visualize failures in CircleCI pipelines

This information gives you more context for why an issue occurred, so you can quickly distinguish between code and pipeline issues and inform the appropriate team of the problem. This not only helps in reducing the number of interruptions to your teams but also decreases the amount of time it takes to resolve the issue.

Reduce alert fatigue with pipeline-specific notifications

CI environments generate a large volume of notifications, which can create alert fatigue and cause your team to miss legitimate issues. You can already create alerts based on CircleCI metrics, but you will soon be able to leverage CircleCI events to build more powerful, pipeline-specific alerts that fit your teams, the jobs they maintain, and the activity they care about. For example, instead of triggering a general alert anytime a pipeline fails, you can alert a specific team when a job within a workflow they manage takes longer than expected to execute.

You can also use Datadog’s turn-key integrations for popular communication platforms like Slack to ping a specific channel when an issue occurs. This helps cut down on the number of notifications that are triggered in your CI environment and ensures that performance issues or anomalies are routed to the appropriate teams for troubleshooting.

Better visibility into your CircleCI environment

With our partnership with CircleCI, you have better visibility into the status and performance of workflows and jobs. Datadog not only makes it easy to compare workflow or job performance across your entire CircleCI environment but also gives you deeper insight into the causes of a failure or performance degradation. This allows you to improve your CI pipelines’ day-to-day performance and reduce the number of environment issues that would otherwise cost your team more time and effort to address. Check out our documentation to learn more about our CircleCI integration, or sign up for a 14-day free trial today.