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

GbyAI
GbyAI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
C
Cisco Blogs
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
IT之家
IT之家
博客园 - 【当耐特】
V
V2EX
博客园_首页
T
Tailwind CSS Blog
Last Week in AI
Last Week in AI
G
Google Developers Blog
The Last Watchdog
The Last Watchdog
C
CXSECURITY Database RSS Feed - CXSecurity.com
博客园 - 司徒正美
N
Netflix TechBlog - Medium
F
Fortinet All Blogs
Know Your Adversary
Know Your Adversary
S
Schneier on Security
V
Vulnerabilities – Threatpost
T
The Exploit Database - CXSecurity.com
Vercel News
Vercel News
量子位
G
GRAHAM CLULEY
T
Threatpost
D
Darknet – Hacking Tools, Hacker News & Cyber Security
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
C
Cybersecurity and Infrastructure Security Agency CISA
S
Security @ Cisco Blogs
B
Blog
Stack Overflow Blog
Stack Overflow Blog
T
Tor Project blog
A
About on SuperTechFans
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
月光博客
月光博客
S
Securelist
博客园 - 聂微东
Cloudbric
Cloudbric
N
News and Events Feed by Topic
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
H
Help Net Security
N
News | PayPal Newsroom
P
Privacy & Cybersecurity Law Blog
Schneier on Security
Schneier on Security
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
W
WeLiveSecurity
Martin Fowler
Martin Fowler
K
Kaspersky official blog
S
Security Affairs
TaoSecurity Blog
TaoSecurity Blog

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
Analyze multiple user journeys with Datadog Pathways diagrams
2024-03-12 · via Datadog | The Monitor blog

Funnels can be powerful tools for analyzing your UX, but figuring out exactly which user journeys you want to study can be challenging. Even if you have an ideal journey in mind, users often take steps you don’t expect. As a result, your funnels—and therefore, your optimization efforts—can easily miss the most influential pages in your application. Indeed, how do you build the best possible funnel when there are thousands of paths users can take after any given page?

Pathways diagrams in Datadog Product Analytics give you high-level overviews of common paths users take in your app, helping you find key journeys to further analyze. You can view a graph based on your most popular views, or generate customized visualizations by selecting a page on your site to view paths to or from. With Pathways, you can identify which routes users find the most accessible, whether that’s because these paths have the most intuitive navigation or because they provide the most visually pleasing experience. When you decide on the journeys you want to study in greater depth, you can then easily create funnels directly from a Pathways diagram or pivot to Datadog Real User Monitoring (RUM) to determine exactly which factors—such as design choices or performance issues—might be driving users to choose one route or another.

In this post, we’ll show you how you can use Pathways to:

Pathways diagrams in Product Analytics show the flow of users throughout your system as they navigate your app. The wildcard option is selected by default for Pathways—with this option, Datadog searches through all the views in your app and automatically highlights the most popular journeys. This makes it easy to determine a starting point for your UX analyses when you don’t have a predefined sequence in mind.

By choosing a specific view, however, you can easily customize Pathways visualizations. Simply select a page in your app, then determine whether you want this to be the starting or ending point for your diagram. To change the scope of your diagram, you can also specify the total number of steps you want to see before or after the selected page, as well as the maximum number of views displayed per step. For more in-depth analyses on certain segments of your user base, you can also filter the sessions included in your diagram by characteristics such as device type, environment, country, and software version.

A Sankey visualization graph showing the steps users took before visiting the cart page, filtered to desktop US user sessions.

Let’s say that you’re attempting to direct users to a page displaying seasonal inventory in your shopping app. You’ve set up a banner on your homepage advertising your seasonal offerings, but you want to see whether users are actually finding it useful. By creating a Pathways diagram with the seasonal inventory page as the endpoint, you can see that users are actually arriving there through the deals page instead of the homepage as you had intended. This gives you a starting point for further UX design decisions. For example, maybe you want to test out giving your homepage banner a more prominent position. Or, alternatively, perhaps you want to focus on making the deals page even more accessible and well-designed to help users more easily find the information you want them to see via the routes they already prefer.

Easily generate and analyze funnels with Product Analytics

When you’ve identified the journeys from your Pathways diagram that you want to study, you can easily leverage Datadog Product Analytics for deeper analysis. For example, to take a closer look at one of these journeys, you can create a funnel directly from a Pathways diagram by clicking the Build Funnel button and then selecting the views you want to incorporate. You’re then able to look at detailed funnel data based on the journey you chose. By clicking into a funnel step, you can view a list of sessions for users who converted or dropped at this point. You can click on any of these sessions to view a replay, helping you see what your users are experiencing and better understand what might have influenced their decisions. The funnel side panel also enables you to easily query RUM Analytics for any of these sessions to view more granular metrics.

A list of converted user sessions for a step in a funnel.

Let’s say that while looking at a Pathways diagram, you discover that the seasonal inventory page is the most popular view in your app. By clicking on the step for the inventory page, you’re able to quickly access session replays for this view. You watch a few session replays to see where on the page your users are spending most of their time, then decide to pivot to a Heatmap from one of these replays to visualize your user interactions in aggregate. Here, you can see a ranking of the top-clicked elements on the seasonal inventory page, enabling you to dig deeper into user trends.

Create useful funnels with Datadog Pathways

With Pathways diagrams in Datadog Product Analytics, you can easily explore all the different routes your users took, helping you design more relevant funnels that provide insight into your most popular paths. You can create funnels directly from the Pathways page when you want to dive deeper into a specific path, or use RUM tools to dig deeper into your UX findings.

To get started with Datadog Pathways, you can use our documentation. Or, if you’re not yet a Datadog user, you can sign 14-day free trial today.