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

博客园_首页
The GitHub Blog
The GitHub Blog
美团技术团队
Know Your Adversary
Know Your Adversary
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
The Register - Security
The Register - Security
Stack Overflow Blog
Stack Overflow Blog
Attack and Defense Labs
Attack and Defense Labs
G
Google Developers Blog
I
InfoQ
博客园 - 司徒正美
T
Troy Hunt's Blog
Google DeepMind News
Google DeepMind News
J
Java Code Geeks
MongoDB | Blog
MongoDB | Blog
博客园 - 聂微东
A
About on SuperTechFans
云风的 BLOG
云风的 BLOG
S
Security Affairs
M
MIT News - Artificial intelligence
Simon Willison's Weblog
Simon Willison's Weblog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
T
Tailwind CSS Blog
量子位
Vercel News
Vercel News
月光博客
月光博客
V
Vulnerabilities – Threatpost
N
News and Events Feed by Topic
Hugging Face - Blog
Hugging Face - Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
L
LangChain Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
L
LINUX DO - 最新话题
F
Full Disclosure
The Hacker News
The Hacker News
Hacker News: Ask HN
Hacker News: Ask HN
T
Tor Project blog
A
Arctic Wolf
Application and Cybersecurity Blog
Application and Cybersecurity Blog
Forbes - Security
Forbes - Security
IT之家
IT之家
Apple Machine Learning Research
Apple Machine Learning Research
B
Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Y
Y Combinator Blog
GbyAI
GbyAI
B
Blog RSS Feed
V
Visual Studio Blog
T
The Blog of Author Tim Ferriss
F
Fortinet All Blogs

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
Introducing Datadog Cloud Network Monitoring
2019-07-17 · via Datadog | The Monitor blog

Editor’s note: This post covers Cloud Network Monitoring, a Datadog feature that was originally called Network Performance Monitoring.

Your applications and infrastructure components rely on one another in an increasingly complex fabric, regardless of whether you run a monolithic application or microservices, and whether you deploy to cloud infrastructure, private data centers, or both. Virtualized infrastructure enables developers to respond to arbitrary scale—and creates dynamic network patterns that aren’t well matched to traditional network monitoring tools. To provide visibility into every component in your environment, and all the connections between them, Datadog is introducing Cloud Network Monitoring.

From granular network details to global aggregates

Visualize the flow from service to service using Network Performance Monitoring.

Datadog Cloud Network Monitoring provides multi-cloud visibility into network flows in granular detail, while also enabling you to aggregate and monitor that data using any tag available in Datadog. So you can query and aggregate connection metrics between any two objects—from services to availability zones, or from Kubernetes pods to security groups—to provide immediate insight into performance and dependencies. Cloud Network Monitoring is fully integrated with the rest of the Datadog platform, so you can view automatically correlated logs and request traces to see the actual requests and application activity that the network traffic represents.

Observing long-lived abstractions

Zoom in on any service in Network Performance Monitoring for a detailed look at its dependencies.

Monitoring dynamic infrastructure means monitoring abstract objects. Individual hosts and containers scale up and down, and IP addresses change, but with tags you can focus your monitoring on longer-lived abstractions like services, applications, and availability zones. After all, if a single container is resource constrained it will be reaped and re-orchestrated, but if an entire service or availability zone is having trouble communicating to a gateway, your customers may be experiencing intermittent timeouts.

In the new Network view in Datadog, you can see the network volume and throughput between any two sets of tags. Datadog automatically collects relevant tags from more than 1,000 integrations, in addition to the custom tags provided directly by developer instrumentation.

Optimize network traffic patterns

Track the flow of data between availability zones and data centers in Network Performance Monitoring to improve performance and reduce transport costs.

Using tags automatically applied to your cloud services and resources, you can instantly sort and filter your metrics to see, for example, how network traffic flows across availability zones for a particular service or for your entire infrastructure. Often, communicating across data centers or availability zones increases the potential for latency and communication errors, not to mention transit costs. By revealing network patterns that may not reflect the intended design of your application, Cloud Network Monitoring can point to areas for performance optimization and cost savings.

Identify misconfigured services

Dig into logs or request traces for any component or service for network-level troubleshooting.

Cloud Network Monitoring is a powerful tool for zeroing in on the source of network issues. Use TCP retransmit count to quickly identify connectivity issues in your network. In a Kubernetes cluster, for example, where containers are constrained only in their CPU and memory usage, a single container can saturate the network. In a few clicks, you can drill down to the container image that is consuming the most network throughput, and pivot directly to logs or request traces from that service to help identify the root cause.

Full-stack dependency monitoring

Map the flow of network traffic across your application.

Flow analytics is a powerful way of drilling down into not just the metrics describing network communication, but the topology of the network as well. By aggregating all of the flows between objects, Datadog can display network traffic on a directed graph. Using tags, you can visualize network topology by service, Kubernetes deployment, Docker image, Chef role, AWS security group, or any other lens.

Fast and light with eBPF

Traditionally, network-level visibility has come with a performance cost—monitoring the flow of packets can chew up significant CPU resources. Datadog’s Cloud Network Monitoring is built on eBPF, which enables detailed visibility into network flows with extremely low overhead. So you can get unprecedented visibility into your network connections in any environment, without the performance trade-offs.

Network observability for the cloud age

If you’re already using Datadog to monitor your applications and infrastructure, you can enable Cloud Network Monitoring by following the steps outlined in our documentation. If you don’t yet have a Datadog account, sign up for a full-featured trial here.