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

Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
爱范儿
爱范儿
H
Help Net Security
Last Week in AI
Last Week in AI
The Cloudflare Blog
博客园 - 三生石上(FineUI控件)
小众软件
小众软件
IT之家
IT之家
aimingoo的专栏
aimingoo的专栏
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
Google DeepMind News
Google DeepMind News
B
Blog
C
Check Point Blog
T
Tailwind CSS Blog
云风的 BLOG
云风的 BLOG
D
Docker
Recent Announcements
Recent Announcements
Vercel News
Vercel News
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
MyScale Blog
MyScale Blog
The GitHub Blog
The GitHub Blog
Stack Overflow Blog
Stack Overflow Blog
雷峰网
雷峰网
人人都是产品经理
人人都是产品经理
月光博客
月光博客
F
Fortinet All Blogs
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed
The Register - Security
The Register - Security
V
Visual Studio Blog
F
Full Disclosure
Hugging Face - Blog
Hugging Face - Blog
T
Threat Research - Cisco Blogs
Latest news
Latest news
PCI Perspectives
PCI Perspectives
Cisco Talos Blog
Cisco Talos Blog
博客园 - Franky
D
DataBreaches.Net
A
Arctic Wolf
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
G
Google Developers Blog
P
Palo Alto Networks Blog
Engineering at Meta
Engineering at Meta
Microsoft Azure Blog
Microsoft Azure Blog
T
Tenable Blog
L
LINUX DO - 热门话题
Spread Privacy
Spread Privacy

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
New feature roundup: Visualization and collaboration
2017-07-13 · via Datadog | The Monitor blog

This is the final post in a three-part series about Datadog’s recent feature enhancements. This post highlights our latest visualization and collaboration features. The other installments in the series focus on new integrations and data collection features and alerting enhancements, respectively.

Datadog aggregates data from more than 1,000 technologies (and growing!), and enables you to create intelligent, custom alerts to automatically detect issues in your environment. But data collection and alerting are only part of the picture—you also need to be able to visualize performance, and collaborate with others to remedy issues and document your findings. In this article, we will highlight a few of the latest additions to the Datadog platform that have helped our users visualize their metrics and collaborate with team members to investigate issues in real time:

Collaboration & exploration with Notebooks

Fostering a postmortem culture is a great way to improve the reliability and resilience of your services. It can also prepare your team to respond more effectively to incidents. Our new Notebooks feature is a great way to explore, investigate, and document your findings in a visual, collaborative format that’s accessible to your whole team.

Anyone can contribute and add feedback to Notebooks, which helps them evolve into rich resources for your team. Notebooks are formatted in Markdown, and supplemented with real-time or historical Datadog graphs for additional context. You can use Notebooks to:

  • create descriptive postmortems that record specific incidents, their underlying causes, any steps that were taken to resolve the situation, and recommended actions to prevent the same issue from reoccurring
  • compose detailed runbooks that outline procedures and step-by-step instructions for fixing the issue(s)
  • graph metrics in an exploration sandbox, without editing or creating dashboards

Notebooks make it easy for teams to create up-to-date, easily accessible internal documentation that prepares them to respond to incidents more quickly. Click the Notebook icon in the Datadog nav bar to try it out.

New ways to visualize application performance

As part of our expansion into application performance monitoring, we developed two new types of visualizations to help you gather insights about application performance: flame graphs and service-level dashboards.

Trace the path of requests in flame graphs

Datadog APM collects traces from each of your services and helps you visualize their performance in detailed flame graphs. Each flame graph breaks down the latency of a request, as well as the time spent accessing various databases, caches, and other services across your environment. Drilling down into a flame graph of a problematic request allows you to pinpoint which services or calls are slowing down user requests or returning errors.

Each span in a request trace includes detailed metadata, such as the actual SQL query executed.
Datadog flame graph of request
Each span in a request trace includes detailed metadata, such as the actual SQL query executed.

Service-level dashboards

Service-level dashboards provide an immediate overview of the health and performance of your applications’ underlying services. Each service you’re monitoring with Datadog APM will automatically generate its own out-of-the-box dashboard, which displays graphs of throughput, errors, and latency, as well as a histogram of sampled latency values.

Datadog dashboard: service-level dashboard

You can also combine infrastructure and application-level metrics in a customizable Datadog dashboard—simply drag and drop the Service Summary widget onto any screenboard.

Datadog dashboard: combine service-level performance with infrastructure-wide metrics in one dashboard
The Service Summary widget (top) adds an auto-generated view of service performance to your Datadog dashboards, which you can supplement with infrastructure metric graphs (bottom).
Datadog dashboard: combine service-level performance with infrastructure-wide metrics in one dashboard
The Service Summary widget (top) adds an auto-generated view of service performance to your Datadog dashboards, which you can supplement with infrastructure metric graphs (bottom).

Read-only users

In addition to improving the visualization and collaboration features available to Datadog users, we’ve also added new management tools for account admins (such as SAML-based authentication). One of the most powerful features for expanding data-driven collaboration across your organization is the addition of read-only users.

Although you may share key metrics with people across your company, you may not want everyone to be able to edit your Datadog alerts and dashboards without your knowledge. Instead of having to watch out for unwanted changes, you now have the option to invite read-only users to your Datadog organization. Read-only users can view everything in Datadog, but they are not able to edit or create monitors, dashboards, or Notebooks.

Categorizing your organization into read-only, standard, and admin-level users ensures that everyone can access and create the Datadog resources they need—nothing more, and nothing less.

More to come

Thanks to the hard work of our engineering teams and the Datadog community, you can begin using all of the new features mentioned in this series (or sign up for a free trial) today. Stay tuned for even more exciting developments in monitoring and observability!