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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
Collaborative Bug Fixing with Datadog’s Sentry Integration
Michael Chen · 2013-10-31 · via Datadog | The Monitor blog
Michael Chen

Michael Chen

Error monitoring and tracking are essential for identifying issues in software development and fixing bugs. For uncommon, host-specific problems in particular, the ability to pinpoint the source machine of an error is a hugely important undertaking. Typically, software teams need to pore over long, esoteric log files like the one pictured below in hopes of finding a useful error message. And, that’s just the starting point after the team has become aware that there’s an issue.

log-screenshot-bug-fixes

With Sentry, an event logging platform primarily focused on capturing and aggregating exceptions, error monitoring, recognition and tracking are automated. Notification alerts appear in your inbox as soon as Sentry picks up exceptional events, allowing for quick relief to potentially fatal bugs. Also, Sentry’s dashboard automatically updates events and generates exception stack traces, saving time from activities that would otherwise be undertaken manually. In short, Sentry streamlines the entire debugging process, and allows dev teams to focus their debugging efforts where it counts.

With our Sentry integration, you can send Sentry events to your team on Datadog! Here are some of the benefits of using Datadog with Sentry.

Once configured, new Sentry events will automatically be forwarded to Datadog. Errors will appear on your Datadog event stream, where you and your team can add comments or suggestions on how to fix them. You can also delegate tasks by sending notifications to other team members as a rapid response system. Notifications are delivered as emails with the previous conversation attached to provide context about the issue.

sentry integration

Correlate errors with metrics and data points from other systems

Datadog allows for the creation of dashboards to quickly discover relationships between your errors and system performance. After creating a timeboard in Datadog with a metric of interest from any system you’ve integrated, you can overlay events from Sentry. In the graph below for example, we’ve plotted how exceptional events have impacted the frequency of timeouts. Finding correlations between your errors and metrics can give you a better understanding of unusual metrics timeseries, saving time on detecting sources of future spikes or periods of idle activity.

sentry integration

Create synthetic error dashboards

You can use also Datadog to create custom dashboards with events and data from Sentry. Simply create a new dashboard with an Event Timeline and/or Event Stream widget with the query sources:sentry and add your metrics. An example of a custom dashboard is shown below. Importantly, you can mix and match data from Sentry and any other integrated system into similar dashboards of your own creation.

sentry integration

Adding the Sentry integration to your Datadog account only takes a few minutes. Just follow the simple steps below.

Setting up Datadog’s Sentry integration

  1. In your Sentry account, enable the integration for WebHooks.

sentry integration
  1. On the WebHooks integration page, add your webhook URL as https://app.datadoghq.com/intake/webhook/sentry?api_key={API_KEY}. Your Datadog API key can be found on the settings page.

sentry integration

And you’re done!

You can improve your team’s debugging experience, and resolve issues faster by signing up for a free trial of Datadog and adding the Sentry integration. The whole process should take just minutes.