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

L
LangChain Blog
N
News and Events Feed by Topic
T
Tor Project blog
AI
AI
S
Schneier on Security
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Hacker News: Ask HN
Hacker News: Ask HN
Spread Privacy
Spread Privacy
The Last Watchdog
The Last Watchdog
B
Blog
G
GRAHAM CLULEY
Recent Commits to openclaw:main
Recent Commits to openclaw:main
W
WeLiveSecurity
GbyAI
GbyAI
Hacker News - Newest:
Hacker News - Newest: "LLM"
C
Check Point Blog
SecWiki News
SecWiki News
Y
Y Combinator Blog
I
Intezer
S
Securelist
WordPress大学
WordPress大学
小众软件
小众软件
Google DeepMind News
Google DeepMind News
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
美团技术团队
Schneier on Security
Schneier on Security
F
Full Disclosure
D
Darknet – Hacking Tools, Hacker News & Cyber Security
P
Proofpoint News Feed
Jina AI
Jina AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Help Net Security
Help Net Security
P
Privacy International News Feed
N
News and Events Feed by Topic
人人都是产品经理
人人都是产品经理
The Register - Security
The Register - Security
Application and Cybersecurity Blog
Application and Cybersecurity Blog
MyScale Blog
MyScale Blog
Vercel News
Vercel News
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Threatpost
H
Help Net Security
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
G
Google Developers Blog
T
Tailwind CSS Blog
L
Lohrmann on Cybersecurity
Forbes - Security
Forbes - Security
C
Cisco Blogs
The GitHub Blog
The GitHub Blog
博客园 - 司徒正美

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
Datadog Guide to AWS re:Invent 2015
2015-09-30 · via Datadog | The Monitor blog

With a week to go until AWS re:Invent and with over 380 events in the session catalog, navigating the schedule can be a bit daunting. But there’s no need to fear, Datadog is here with our session picks for the 2015 AWS re:Invent conference.

But don’t spend all your time at sessions—some of the most interesting advances will be at vendor booths. For example, if you stop by the Datadog booth (which, trust us, you can’t miss), we’ll give you a live demo of our newly announced outlier detection features and new AWS integrations such as Elastic Container Service and DynamoDB. We also invite you to join us at Aquaknox on Wednesday night during the AWS Pub Crawl.

Sessions by Datadog

DVO 205 - Monitoring Evolution

Wednesday, Oct 7, 11:00 AM – Delfino 4005

Learn how the AdRoll team became a data driven organization using Datadog, EC2’s dynamic infrastructure, and related tooling. Speakers: Ilan Rabinovitch (Dir, Technical Community @ Datadog) and Brian Troutwine (Sr. Software Engineer @ AdRoll).

DVO 204 - Monitoring Strategies: Finding Signal in the Noise

Thursday, Oct 8, 11:00 AM – Murano 3305

Avoid pager fatigue with a framework for identifying what to monitor, what to alert on, and resources that will help you perform root-cause analysis. Speaker: our very own Matt Williams.

Our Schedule

So, where will you find Datadog team members when we are not at our booth? Here is our personal re:Invent schedule:

Wednesday

DVO 205 - Monitoring Evolution

Wednesday, Oct 7, 11:00 AM – Delfino 4005

Today, AdRoll runs its infrastructure by instrumentation: constantly asking empirical questions, analyzing data for answers, and designing new features with instrumentation in mind to understand how functionality will work upon release. AdRoll’s development methodology did not start out this way, however. It took a cultural shift and many new tools and processes to adopt this approach. In this session, AdRoll and Datadog will discuss how to evolve your organization from a state of “flying blind” to a culture focused on monitoring and data-based decisions.

DVO202 - DevOps at Amazon: A Look at Our Tools and Processes

Wednesday, Oct 7, 12:15 PM – Venetian H

As software teams transition to cloud-based architectures and adopt more agile processes, the tools they need to support their development cycles will change. In this session, we’ll take you through the transition that Amazon made to a service-oriented architecture over a decade ago. We will share the lessons we learned, the processes we adopted, and the tools we built to increase both our agility and reliability. We will also introduce you to AWS CodeCommit, AWS CodePipeline, and AWS CodeDeploy, three new services born out of Amazon’s internal DevOps experience.

CMP401- Elastic Load Balancing Deep Dive and Best Practices

Wednesday, Oct 7, 1:30 PM – Palazzo N

Elastic Load Balancing automatically distributes incoming application traffic across multiple Amazon EC2 instances for fault tolerance and load distribution. In this session, we go into detail about Elastic Load Balancing’s configuration and day-to-day management, as well as its use in conjunction with Auto Scaling. We explain how to make decisions about the service and share best practices and useful tips for success.

CMP307 - Using Spot Instances for Production Workloads

Wednesday, Oct 7, 2:45 PM – San Polo 3506

Spot instances have come a long way since they were first introduced. Leveraging multiple Auto Scaling groups along with AWS functionality enhancements, you can even use them effectively for real-time production workloads. The higher the flexibility of the workload, the greater the cost savings are in comparison to a conventional combination of Reserved and On-Demand instances. Join us in this session to explore these techniques along with configuration approaches that allow you to tune the risk/reward balance.

DVO203 - A Day in the Life of a Netflix Engineer Using 37% of the Internet

Wednesday, Oct 7, 4:15 PM – Venetian H

Netflix is a large and ever-changing ecosystem made up of: hundreds of production changes every hour, thousands of micro services, tens of thousands of instances, millions of concurrent customers, billions of metrics every minute. And I’m the guy with the pager. This is an in-the-trenches look at what operating at Netflix scale in the cloud is really like. It covers how Netflix views the velocity of innovation, expected failures, high availability, engineer responsibility, and obsessing over the quality of the customer experience. It also explains why freedom and responsibility are key, trust is required, and chaos is your friend.

GEN102 - Pub Crawl

Wednesday, Oct 7, 5:30 PM–7:30 PM – Aquanox

Join AWS and Data sponsors at the best clubs and restaurants in the Venetian and Palazzo. Hosted bar and appetizers on the house.

Thursday

DVO 204 - Monitoring Strategies: Finding Signal in the Noise

Thursday, Oct 8, 11:00 AM – Murano 3305

You need to monitor only a few machines and applications before fixing issues in your environment becomes very complicated. Throw in the type of dynamic infrastructure provided by Amazon EC2, and your static monitoring strategies will most likely not scale. Knowing which metrics to watch and how to troubleshoot based on those metrics will help you solve problems more quickly. In this session, we will look at a framework for your metrics and how to use it to find solutions to the issues that come up. We will cover the three types of monitoring data; what to collect; what should trigger an alert (avoiding an alert storm); and how to follow the resources to find the root causes of problems.

CMP406 - Amazon ECS at Coursera: Modifying the ECS Agent for Production

Thursday, Oct 8, 1:30 PM – San Polo 3506

Come see how Coursera modified the Amazon EC2 Container Service (Amazon ECS) Agent to fit complex post-processing and security requirements. Anyone can sign up for a course on Coursera for free, so our offerings require that we defend against arbitrary code execution within our containers. To do so, we adopt a defense-in-depth approach where multiple layers of security are used to prevent bad actors from abusing the system. As part of this approach, we have modified the Amazon ECS Agent to run untrusted code within the Docker containers to grade programming assignment submissions from users around the world. We also modified the ECS agent in a separate fork to support running Docker within Docker, which we do to post-process uploaded grading templates from instructional teams to harden them against other attacks and prepare them for execution within our hardened grading environment. In this session, we outline our approach, providing ideas for your own post-processing and hardening requirements.

CMP302 - Amazon EC2 Container Service: Distributed Applications at Scale

Thursday, Oct 8, 2:45 PM – Venetian H

In recent years, containers have become a key component of modern application design. Increasingly, developers are breaking their applications apart into smaller components and distributing them across a pool of compute resources. It is relatively easy to run a few containers on your laptop, but building and maintaining an entire infrastructure to run and manage distributed applications is hard and requires a lot of undifferentiated heavy lifting. In this session, we discuss some of the core architectural principles underlying Amazon ECS, a highly scalable, high performance service to run and manage distributed applications using the Docker container engine. We walk through a number of patterns used by our customers to run their microservices platforms, to run batch jobs, and for deployments and continuous integration. We explore the advanced scheduling capabilities of Amazon ECS and dive deep into the Amazon ECS Service Scheduler, which optimizes for long-running applications by monitoring container health, restarting failed containers, and load balancing across containers.

BDT403 - Best Practices for Building Real-time Streaming Applications with Amazon Kinesis

Thursday, Oct 8, 4:15 PM – Palazzo F

Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. Customers who use Amazon Kinesis can continuously capture and process real-time data such as website clickstreams, financial transactions, social media feeds, IT logs, location-tracking events, and more. In this session, we first focus on building a scalable, durable streaming data ingest workflow, from data producers like mobile devices, servers, or even a web browser, using the right tool for the right job. Then, we cover code design that minimizes duplicates and achieves exactly-once processing semantics in your elastic stream-processing application, built with the Kinesis Client Library. Attend this session to learn best practices for building a real-time streaming data architecture with Amazon Kinesis, and get answers to technical questions frequently asked by those starting to process streaming events.

Friday

ARC302 - Running Lean Architectures: How to Optimize for Cost Efficiency

Friday, Oct 9, 9:00 AM – Palazzo K

Come see how Coursera modified the Amazon EC2 Container Service (Amazon ECS) Agent to fit complex post-processing and security requirements. Anyone can sign up for a course on Coursera for free, so our offerings require that we defend against arbitrary code execution within our containers. To do so, we adopt a defense-in-depth approach where multiple layers of security are used to prevent bad actors from abusing the system. As part of this approach, we have modified the Amazon ECS Agent to run untrusted code within the Docker containers to grade programming assignment submissions from users around the world. We also modified the ECS agent in a separate fork to support running Docker within Docker, which we do to post-process uploaded grading templates from instructional teams to harden them against other attacks and prepare them for execution within our hardened grading environment. In this session, we outline our approach, providing ideas for your own post-processing and hardening requirements.

DAT304 - Amazon RDS MySQL: Best practices

Friday, Oct 9, 10:15 AM – Delfino 4102

Learn how to monitor your database performance closely and troubleshoot database issues quickly using a variety of features provided by Amazon RDS and MySQL including database events, logs, and engine-specific features. You will also learn about the security best practices to use with Amazon RDS for MySQL as well as how to effectively move data between Amazon RDS and on-premises instances. Hear from Amazon RDS customer Airbnb about the best practices they have implemented in their RDS for MySQL architectures.

CMP310 - Building Robust Data Processing Pipelines Using Containers and Spot Instances

Friday, Oct 9, 11:30 AM – San Polo 3506

It’s difficult to find off-the-shelf, open-source solutions for creating lean, simple, and language-agnostic data-processing pipelines for machine learning (ML). This session shows you how to use Amazon S3, Docker, Amazon EC2, Auto Scaling, and a number of open source libraries as cornerstones to build one. We also share our experience creating elastically scalable and robust ML infrastructure leveraging the Spot instance market.

Sessions by Datadog Customers

AdRoll

DVO 205 - Monitoring Evolution (Brian Troutwine)

Wednesday, Oct 7, 11:00 AM – Delfino 4005

CMP310 - Building Robust Data Processing Pipelines Using Containers and Spot Instances (Oleg Avdeev)

Friday, Oct 9, 11:30 AM – San Polo 3506

Coursera

CMP406 - Amazon ECS at Coursera: Modifying the ECS Agent for Production

Thursday, Oct 8, 1:30 PM – San Polo 3506

Team Internet

ARC302 - Running Lean Architectures: How to Optimize for Cost Efficiency

Friday, Oct 9, 9:00 AM – Palazzo K