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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
Expand your Sleuth monitoring reach with Datadog
2020-10-30 · via Datadog | The Monitor blog

Sleuth is a deployment tracking tool that gives you a deeper level of insight into your CI/CD workflows by tracking all of your team’s deployment tools from a single dashboard. Sleuth integrates with different components of your deployment pipeline and develops an understanding of your development processes. It can then automatically alert you as to when code is shipping, when manual approvals are needed, and when failures occur. Sleuth is built to work with a wide array of tools from across the code deployment toolkit, reducing the friction that occurs between code bases (like Github and Bitbucket), issue trackers (like Jira and Clubhouse), feature flaggers (like LaunchDarkly), and infrastructure-as-code providers (like Terraform).

We’re excited to announce that Datadog now integrates with Sleuth. Sleuth’s Datadog integration is easy to install, and, once configured, allows Sleuth users to tie Datadog’s rich metrics to the various sources of change that Sleuth monitors. From code and issue-tracking to feature flags and more, Sleuth can use your Datadog metrics to provide greater context around a feature or release from first concept to development, testing, and push to production

Full visibility into your Sleuth workflow

Sleuth organizes your deployments into projects, which collect and organize key data from your code sources and their associated staging environments. This data consists of metrics and errors. Metrics that Sleuth pulls from your deployment infrastructure might include the average response times of your APIs or the percentage of notifications sent within a five-minute SLA, while errors might include the rate of service errors sampled from across every active deployment in the project. Sleuth then uses this data to make smart inferences about which changes caused a chosen data fluctuation.

When you install the Datadog integration in Sleuth, Sleuth immediately begins pulling in the Datadog metrics of your choosing and attaching them to their related deployments. This gives you a big-picture perspective of your project’s health as measured across past and current deployments and allows Sleuth to detect unusual activity within your deployments and the tools you use to automate them. And, because Datadog integrates with many of the same services that Sleuth does, as well as more than 1,000 other technologies, you can enrich your Sleuth with monitoring data from across your deployment stack.

Visualize key Datadog performance metrics

Sleuth’s Datadog integration gives you a comprehensive view of your projects’ performance and lets you analyze incoming service level indicator (SLI) data across all of your Sleuth-integrated services from a single location. The default release window contains widgets that display key metrics like the average time elapsed for PR approval and the rate of errors within your deployment testing environments. You can easily customize the window to include visualizations of metrics from Datadog or other parts of your stack that you want to track, giving you insight into the metric variances, both large and small, that affect your deployments.

Sleuth's Datadog integration lets you visualize the deployment metrics it pulls from your Datadog monitoring infrastructure.

Elevate your Sleuth performance with Datadog

Sleuth’s Datadog integration gives you unparalleled clarity into your deployment workflow and expands Sleuth’s deployment tracking capabilities. And by integrating with over 1,000 other technologies, Datadog helps you detect errors across your entire stack and understand how they might be affecting your deployments. If you’re already a Datadog customer, you can start using the Sleuth integration right now. Otherwise, get started with a free 14-day trial.