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

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
T
Troy Hunt's Blog
P
Proofpoint News Feed
V
Vulnerabilities – Threatpost
C
Cybersecurity and Infrastructure Security Agency CISA
K
Kaspersky official blog
Cyberwarzone
Cyberwarzone
T
Tor Project blog
Cisco Talos Blog
Cisco Talos Blog
S
Securelist
L
Lohrmann on Cybersecurity
Security Latest
Security Latest
T
Threatpost
H
Heimdal Security Blog
W
WeLiveSecurity
A
Arctic Wolf
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
G
GRAHAM CLULEY
IT之家
IT之家
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
TaoSecurity Blog
TaoSecurity Blog
A
About on SuperTechFans
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
N
News and Events Feed by Topic
Hacker News - Newest:
Hacker News - Newest: "LLM"
Last Week in AI
Last Week in AI
T
The Blog of Author Tim Ferriss
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Microsoft Azure Blog
Microsoft Azure Blog
Hugging Face - Blog
Hugging Face - Blog
Google DeepMind News
Google DeepMind News
量子位
Stack Overflow Blog
Stack Overflow Blog
Know Your Adversary
Know Your Adversary
B
Blog RSS Feed
阮一峰的网络日志
阮一峰的网络日志
WordPress大学
WordPress大学
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
AI
AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 司徒正美
Apple Machine Learning Research
Apple Machine Learning Research
GbyAI
GbyAI
Vercel News
Vercel News
C
Cyber Attacks, Cyber Crime and Cyber Security
Latest news
Latest news
D
Darknet – Hacking Tools, Hacker News & Cyber Security
大猫的无限游戏
大猫的无限游戏
Forbes - Security
Forbes - Security

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 Oracle Arm-based Ampere A1 instances with Datadog
2021-05-28 · via Datadog | The Monitor blog

Arm processors have long been at the center of mobile computing, powering billions of smartphones, tablets, smartwatches, and other IoT devices. Today, these processors are beginning to see broader adoption in the cloud as they promise better performance, higher energy efficiency, and lower costs than their x86-based predecessors. Just this week, Oracle announced its new Oracle Cloud Infrastructure Ampere A1 Compute platform, built on the Ampere Altra Arm processor.

The Datadog Agent for Arm allows you to collect metrics, traces, logs, and more from your Oracle Arm-based Ampere A1 instances (both virtual machine and bare metal), so you can get comprehensive visibility into your entire infrastructure. In this post, we’ll discuss how Datadog can help you:

Monitor migrations to Oracle Arm-based Ampere A1 instances

If you’re migrating existing workloads over to these new Oracle Arm-based Ampere A1 instances, it’s important to compare the health and performance of both your old and new instances to verify that the process has not introduced any regressions. Datadog’s host map provides a bird’s eye view of your entire infrastructure, allowing you to track system-level metrics such as CPU and memory utilization across all of your hosts. You can use any tag you’ve configured to filter and group your hosts for a more organized view of your fleet. For example, if you’re shifting from AMD E3 instances to Ampere A1 instances, you can group your hosts by instance type to easily compare their resource utilization, as shown in the screenshot below.

Use Datadog's host map to compare CPU utilization across instances

Rightsizing your instances to fit your workloads

Whether you’re running a small database or a multi-tier web service, Oracle lets you flexibly configure the number of OCPUs and amount of memory on your Ampere A1 instances to suit your workload requirements. But because these resources are billed at a per-second granularity, you’ll want to optimize your instance configuration for both performance and cost.

Configuring the OCPU and memory allocation of an Ampere A1 instance in the Oracle Cloud Infrastructure Console
Oracle allows you to configure OCPU and memory on its Ampere A1 instances
Configuring the OCPU and memory allocation of an Ampere A1 instance in the Oracle Cloud Infrastructure Console

Datadog APM collects request traces and performance metrics such as throughput, error rate, and latency, so you can better understand how your applications are responding to incoming traffic. You can also view this data in context with system-level metrics to determine whether your instances’ available resources can support your workloads. This enables you to identify and proactively scale up any resource-constrained instances to avoid potential performance degradations. Similarly, you can use APM alongside the host map to identify over-provisioned instances, which can be scaled down to save costs.

Use Datadog APM to monitor service-level metrics like request count, latency, and errors

Optimize resource-intensive parts of your application

While Datadog’s host maps and infrastructure metrics can alert you to resource saturation, Live Processes allows you to pinpoint the most resource-intensive processes, which may be preventing other processes from running efficiently. In Live Processes, you can dive into every process across your distributed system in one place, without having to SSH into each individual host. Datadog also lets you visualize processes running on a host in a tree format (similar to what you’d get from running htop), enabling you to easily spot orphan processes that can be terminated. Additionally, you can generate metrics from these processes to track and analyze trends in resource consumption over the long term.

Live Processes lets you view processes across all your hosts to pinpoint

Datadog Continuous Profiler goes one step further by providing granular, code-level insights. As shown in the screenshot below, each profile contains a collection of stack traces that reveal which methods or packages in your code are consuming the most resources. And because Continuous Profiler collects data from all of your hosts without interruptions, you can track how code modifications affect performance over time.

A profile that visualize which methods in your code are taking up the most CPU time

Start monitoring Oracle Arm-based Ampere A1 instances with Datadog today

Datadog’s support for Oracle Arm-based Ampere A1 instances extends your infrastructure coverage and allows you to monitor all your virtual machines, on-premise servers, containers, and more in one place. And with our 1,000+ out-of-the-box integrations, including Oracle Database and MySQL, you can see how the performance of your infrastructure impacts the services running on it.

If you’re an existing Datadog customer, you can deploy the Datadog Agent for Arm on your Ampere A1 instances right away. You can also read our joint blog post with Oracle for more useful resources. New to Datadog? Get started with a 14-day free trial today.