惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Heimdal Security Blog
P
Privacy International News Feed
S
Schneier on Security
P
Proofpoint News Feed
L
Lohrmann on Cybersecurity
Spread Privacy
Spread Privacy
P
Privacy & Cybersecurity Law Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Scott Helme
Scott Helme
K
Kaspersky official blog
大猫的无限游戏
大猫的无限游戏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
aimingoo的专栏
aimingoo的专栏
Simon Willison's Weblog
Simon Willison's Weblog
S
Securelist
Help Net Security
Help Net Security
B
Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Security Archives - TechRepublic
Security Archives - TechRepublic
云风的 BLOG
云风的 BLOG
The GitHub Blog
The GitHub Blog
N
News and Events Feed by Topic
Hacker News: Ask HN
Hacker News: Ask HN
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
M
MIT News - Artificial intelligence
雷峰网
雷峰网
博客园 - 司徒正美
V
V2EX
AWS News Blog
AWS News Blog
Know Your Adversary
Know Your Adversary
N
News | PayPal Newsroom
T
Tor Project blog
Cisco Talos Blog
Cisco Talos Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
PCI Perspectives
PCI Perspectives
Google DeepMind News
Google DeepMind News
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
U
Unit 42
C
Cybersecurity and Infrastructure Security Agency CISA
P
Palo Alto Networks Blog
G
Google Developers Blog
T
Threat Research - Cisco Blogs
博客园 - Franky
I
InfoQ
D
DataBreaches.Net
爱范儿
爱范儿
Y
Y Combinator Blog
博客园 - 叶小钗
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报

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
Optimize your frontend monitoring strategy with Datadog Synthetic Monitoring and RUM
2023-06-12 · via Datadog | The Monitor blog

Testing enables you to proactively identify and resolve issues before they break critical functionality in your application, which is essential to ensuring an optimized user experience (UX). However, if you don’t know how users are actually interacting with your application, key user journeys may go untested. This lack of visibility can lead to a proliferation of unoptimized features in your UI, causing users to drop off before completing important actions. Additionally, the inability to accurately correlate test and user data can complicate your real-user monitoring (RUM) activity, meaning you may encounter challenges when troubleshooting frontend issues.

Datadog makes it easy to combine observability data from RUM and Synthetic Monitoring for full visibility into your end-user experience. By correlating data from both real-user and synthetic sessions, you can analyze your UX from two different angles: actual and optimal conditions. This enables you to assess the impacts of issues and identify root causes faster. And to prevent issues from affecting your users in the first place, you can leverage RUM data to design more realistic, complete synthetic tests (as shown in the screenshot below).

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

The test creation page for a new browser test in Synthetic Monitoring.

Provide a reliable UX with insights from RUM and synthetic sessions

By using RUM and Synthetic Monitoring together, you have access to frontend experience data from both test and real-user sessions, enabling you to quickly identify the source of issues so that you can deliver a reliable end-user experience. In particular, drilling into Synthetic Monitoring data the same way you would actual user data can help you figure out when and where an issue started. For example, did a problem originate from a bug in a new feature release or from an unpredictable aspect of real-world conditions, such as a problem with a third-party API?

Datadog enables you to view key performance metrics for your RUM and Synthetic Monitoring sessions side-by-side. In RUM, you can view core web vitals, such as Cumulative Layout Shift (CLS) and Largest Contentful Paint (LCP), from both your real-user and synthetic sessions. Monitoring these metrics helps you spot performance issues that may be impacting your users’ ability to interact with your app, such as long or uneven loading times. You can also access session replays directly from session overviews, enabling you to watch visual recreations of synthetic and real-user journeys alongside details of every event in the session.

A synthetic session in RUM, with an overview of views, actions, errors, and frustration signals.

Additionally, you can view these core web vitals alongside metrics tailored to each session type, giving you context for critical actions and events. For Synthetic Monitoring sessions, you can access API test response times as well as global uptime and time-to-interactive data. And for real-user sessions, you can view browser performance metrics like page loading times and error rates. Additionally, the real-user summary also includes frustration signals that can reveal pain points for users in your UI.

By viewing data for both types of sessions within RUM, you can analyze root causes and create meaningful tests. Let’s say you receive feedback that customers are experiencing high loading times when attempting to add items to their carts, leading them to abandon your app out of frustration before actually buying anything. Upon accessing the results of your synthetic sessions in RUM, you can see that this issue also began to appear in your test data shortly after a recent update. From the synthetic session summary, you can pinpoint the action that’s experiencing high latency, then compare replays from this session against real-user sessions to understand the full impact of the problem.

Streamline test design with real-user data

One of the most difficult tasks when designing frontend tests is deciding which user journeys to examine. By analyzing real-user session data from RUM, you can gain better insight into which journeys users are actually taking in your app, enabling you to create useful, relevant tests.

Datadog Test Coverage helps you build effective synthetic tests by leveraging RUM data to reveal discrepancies between your test design and actual user workflows. Test Coverage displays an overview of testing coverage for popular views, as well as a list of actions that are currently included in your synthetic tests. You can also access a summary of untested actions sorted by popularity, with the ability to jump to relevant events in RUM. For more details on your tested actions, you can pivot to related test session replays directly from the Test Coverage page. These features give you increased visibility into which actions and journeys you are—and aren’t—accurately capturing, streamlining your test design process and ensuring complete coverage for your most popular flows.

The Test Coverage overview page in Synthetic Monitoring, showing a summary of views and user actions currently captured by synthetic tests.

Datadog RUM and Synthetic Monitoring, better together

On their own, Datadog RUM and Synthetic Monitoring each provide deep insights into your application’s frontend experience. By combining them, however, you can obtain complete visibility into your user journeys, enabling you to optimize your app and delight your customers. Analyzing real-user and synthetic sessions together gives you two angles from which you can troubleshoot issues and helps you to create useful, effective tests. Datadog makes this easy with features such as synthetic session summaries within RUM, which enable you to dig into test performance, as well as Test Coverage and funnels for insights into your test design.

You can use our documentation to get started with Datadog RUM and Synthetic Monitoring. Or, if you’re not yet a Datadog user, you can sign up for a 14-day free trial today.