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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
Visually replay user-facing issues with Zendesk and Datadog Session Replay
Jamie Milstein, Aaron Kaplan · 2024-02-02 · via Datadog | The Monitor blog

Zendesk provides support teams with an integrated solution for processing all types of customer inquiries and feedback. But as organizations scale, support tickets can multiply, making it difficult to parse customer feedback and investigate issues promptly and thoroughly. Customers often report problems without providing the detailed context needed for effective troubleshooting. Support engineers work painstakingly to piece together an accurate picture of user experience, following up repeatedly with individual customers for additional information and aids such as screenshots in order to reproduce bugs. This drawn-out troubleshooting process hinges on guesswork and the time and patience of customers, often yielding dead-ends and frustration.

We’re excited to announce our new integration with Zendesk and Datadog Session Replay. Session Replay captures individual user sessions via telemetry and reconstructs them in a video-like playback interface. With this integration, support engineers can quickly reproduce issues by directly accessing relevant user sessions from any Zendesk ticket, eliminating their reliance on customers to provide detailed context when reporting a problem and enhancing the speed and precision of their troubleshooting.

Home in on customer-reported issues with precision

Once you’ve set up Session Replay in Datadog and installed the integration from the Zendesk marketplace, you can access the relevant user sessions for any support ticket by selecting that ticket in Zendesk and then selecting Datadog RUM from the right-hand sidebar.

Selecting a session from this list or clicking “View all sessions” takes you to Datadog RUM, where you can watch a pixel-perfect reproduction of exactly what each user experienced. Each session includes an event timeline that breaks down each page load and DOM change. This direct visibility into and granular breakdown of each session enables you to zero in on the root causes of customer-facing issues without complex tooling, guesswork, or drawn-out deliberations with customers, ultimately helping to minimize the mean time to resolution (MTTR) of customer tickets.

Session Replay also allows you to create playlists, or folder-like aggregations of user sessions. This can come in handy when multiple customers submit tickets reporting the same issue, pointing to an underlying bug. Support teams can group user sessions into Session Replay playlists and share them with product and engineering teams to notify them of the issue and guide their troubleshooting.

Expedite support with a clear picture of customer experience

Integrating Datadog RUM’s Session Replay with Zendesk provides on-demand visibility into individual user sessions, minimizing the burden on both support teams and customers and helping you to improve reliability and customer satisfaction. To get started, you can install our Session Replay integration from the Zendesk Marketplace. You can also check out our dedicated blog post on Session Replay. And if you’re new to Datadog, you can sign up for a 14-day free trial.