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

C
Check Point Blog
GbyAI
GbyAI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
美团技术团队
NISL@THU
NISL@THU
C
Cisco Blogs
SecWiki News
SecWiki News
N
Netflix TechBlog - Medium
Forbes - Security
Forbes - Security
Cloudbric
Cloudbric
雷峰网
雷峰网
T
Tailwind CSS Blog
博客园 - 司徒正美
The Register - Security
The Register - Security
L
LangChain Blog
S
Security Affairs
Hacker News - Newest:
Hacker News - Newest: "LLM"
B
Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
T
Threat Research - Cisco Blogs
I
InfoQ
S
Schneier on Security
L
Lohrmann on Cybersecurity
量子位
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Martin Fowler
Martin Fowler
Schneier on Security
Schneier on Security
F
Fortinet All Blogs
TaoSecurity Blog
TaoSecurity Blog
K
Kaspersky official blog
Google DeepMind News
Google DeepMind News
Cisco Talos Blog
Cisco Talos Blog
PCI Perspectives
PCI Perspectives
Attack and Defense Labs
Attack and Defense Labs
WordPress大学
WordPress大学
Microsoft Azure Blog
Microsoft Azure Blog
H
Help Net Security
Project Zero
Project Zero
The GitHub Blog
The GitHub Blog
D
Docker
N
News | PayPal Newsroom
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
H
Hacker News: Front Page
云风的 BLOG
云风的 BLOG
Microsoft Security Blog
Microsoft Security Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 聂微东
Webroot Blog
Webroot Blog
MongoDB | Blog
MongoDB | 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
Monitor and improve your CI/CD on AWS CodePipeline with Datadog CI Visibility
Kassen Qian, Maxim Brown · 2023-11-27 · via Datadog | The Monitor blog
Kassen Qian

Kassen Qian

Maxim Brown

Maxim Brown

Technical Content Writer

CI/CD services such as AWS CodePipeline enable developers to automate and accelerate the process of building, testing, and deploying code. But with the speed, scale, and complexity of the modern software development life cycle, even small performance regressions or increases in failure rates in your CI system can quickly snowball, slowing or even halting releases and causing cost overruns. End-to-end visibility into your CI pipelines is vital for quickly identifying and triaging problems early before they affect your teams or customers.

Datadog already enables you to get full performance and security visibility across your AWS infrastructure. In this post, we’ll look at how Datadog’s new AWS CodePipeline integration for CI Pipeline Visibility enables you to shift your AWS monitoring further left and:

  • Proactively monitor and improve the health and performance of your pipelines

  • Drill into individual pipelines to locate and identify errors in your stages

  • Create monitors that alert the relevant teams to pipeline failures

Monitor the health and performance of your pipelines

Large development teams may deploy dozens or even hundreds of code changes per day. It’s important to be able to monitor overall performance across the relevant CI pipelines and surface problems like rising duration or error rates, or to identify outliers that you might need to investigate. Once you’ve configured Datadog’s AWS CodePipeline integration for CI Pipeline Visibility, you can use the customizable out-of-the-box CI Pipeline Visibility dashboard to get a bird’s-eye view of your CI system. You can easily filter the dashboard to focus on CI providers (such as AWS CodePipeline) or specific pipelines, branches, or deployment environments. Summaries of your slowest or most failure-prone pipelines, stages, and actions enable you to quickly see where problems are and focus troubleshooting efforts.

Datadog’s CI Visibility pipelines dashboard

Similarly, the CI Pipelines list aggregates all of your instrumented pipelines. Key metrics for duration, executions, and failures enable you to see the performance and reliability of your AWS pipelines and compare them to the rest of your CI system. Sorting the list makes it easy to identify which pipelines are slowest, most active, or least successful across providers.

Datadog’s CI Visibility pipelines list

Drill into your pipelines

If you identify a pipeline that is particularly slow, or that fails frequently, you can drill into it to investigate the possible cause. Pipeline overview pages visualize key metrics, including breakdowns of how many times the pipeline has run over time and how many of those executions experienced errors.

Pipeline detail overview page

By selecting a specific execution, you can access a flame graph that breaks the pipeline execution down into its constituent stages and actions, making it easy to understand which ones are contributing the most to end-to-end execution duration, as well as which experienced errors. This enables you to identify where exactly your pipelines are breaking so you can quickly fix them and continue to push code.

Flame graph of individual pipeline execution showing an error

Route alerts to the relevant teams

Addressing broken pipelines as soon as possible is key to maintaining a consistent release schedule. You can set CI Pipeline monitors to alert you to performance regressions or increased error rates at the pipeline, stage, or action level. Configuring your monitors to route alerts to the teams or individuals who own the pipelines will help ensure that the relevant people will be able to respond more quickly.

Creating a CI Visibility pipeline monitor

Datadog + AWS CodePipeline

CI/CD services like AWS CodePipeline have become vital components of the modern SDLC, enabling teams to release code and new features to customers quickly and more frequently. With Datadog, you can seamlessly monitor your pipelines and spot issues in your development workflows before they impact your entire development team, meaning your org’s software development processes can become more efficient in delivering business value. By integrating AWS CodePipeline with Datadog, you can monitor the health and performance of your CI/CD systems alongside telemetry from the rest of your AWS services from one unified platform. See our documentation to get started with Datadog’s AWS CodePipeline integration for CI Pipeline Visibility. If you’re not using Datadog, sign up for a 14-day free trial.