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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's AWS re:Invent 2019 guide
2019-11-11 · via Datadog | The Monitor blog
Jason Yee

Jason Yee

AWS re:Invent is an annual gathering of tens of thousands of AWS staff, partners, and users for a full week of keynote sessions, feature announcements, customer case studies, hands-on workshops, and more. As in years past, we will be there with dozens of engineers, ready to answer your monitoring questions and show you the newest additions to Datadog.

Datadog's booth at AWS re:Invent.

Learn more & be inspired

The sponsor hall is a great way to learn more about the newest AWS features and partner products, and the conference sessions can be educational and inspiring. AWS re:Invent hosts hundreds of sessions where you can hear about the challenges other organizations have faced and how they’ve solved them.

With so many sessions available, how do you know which ones to attend? Below, we’ve compiled a list of interesting sessions that we’re excited to watch:

Using deep learning to track wildfires and air quality (AIM329)

ALERTWildfire is a camera-based network infrastructure that captures satellite imagery of wildfires. In this chalk talk, we discuss deep-learning techniques that use this satellite imagery along with meteorological data to track wildfires and predict air quality in real time.

How to refactor a monolith to serverless in 8 steps (API310)

Refactoring a monolith to serverless can be intimidating, but there are discrete steps that you can take to simplify the process. In this chalk talk, we outline eight steps for successfully refactoring your monolith and highlight key decision points such as language and tooling choices. Through real-world examples of successful migrations, we uncover common mistakes, useful techniques for identifying components for migration and service boundaries, and processes for migrating large amounts of data without downtime. Bring your refactoring challenges to this interactive session to see how these techniques can be applied in the context of your own application.

Scaling to billions of requests the serverless way at Capital One (DEM34-S)

Stream processing tools like Apache Spark and Flink are the default choice for big data processing, but these frameworks also come with high development and operation costs. Serverless streaming architecture is an alternative solution that brings significant reduction in these costs and allows developers to focus on business delivery, not infrastructure management. This session explores how Capital One used serverless streaming architecture to provide real-time insights for millions of customers through its intelligent assistant Eno. Learn how high-throughput streaming loads can be handled with ease as well as how message-driven architecture can be implemented using Amazon API Gateway, AWS Lambda, and Amazon Kinesis for complex asynchronous applications.

Performing chaos engineering in a serverless world (CMY301)

The principles of chaos engineering have been battle-tested for years using traditional infrastructure and containerized microservices. But how do they work with serverless functions and managed services? In this session, we cover the motivations behind chaos engineering, how we perform chaos experiments, and what some of the common weaknesses are that we can test for in our serverless applications. We also run some actual experiments in a serverless AWS environment. Join us as we move from talking about principles to performing real chaos-engineering experiments for serverless.

BPF performance analysis (OPN303-R)

Extended BPF (eBPF) is an open-source Linux technology that powers a whole new class of software: mini programs that run on events. Among its many uses, BPF can be used to create powerful performance-analysis tools capable of analyzing everything: CPUs, memory, disks, file systems, networking, languages, applications, and more. In this session, Netflix’s Brendan Gregg tours BPF tracing capabilities, including many new open-source performance analysis tools he developed for his new book “BPF Performance Tools: Linux System and Application Observability.” The talk also includes examples of using these tools in the Amazon Elastic Compute Cloud (Amazon EC2) cloud.

The eBPF session is particularly interesting to us, as we’re using eBPF to power our new Network Performance Monitoring tool.

Sessions with Datadog

By taking care of the servers and monitoring infrastructure for you, AWS and Datadog enable you to focus on solving important business problems. Our customers are solving some truly interesting challenges, and we love helping them share their stories. Here are the sessions we’ve partnered on:

More than rubber on the road: Tires in an IoT world (IOT204-S)

Pirelli is known for creating cutting-edge, high-quality tires with a focus on the performance needs of both high-end consumer drivers and professional drivers. But today, Pirelli tires are much more than rubber on the road—they are connected IoT devices reporting telemetry data to help drivers achieve their safety and performance goals. Between this telemetry function and Pirelli’s other applications, a huge amount of data flows into Pirelli’s systems. Ensuring that these platforms are scalable and reliable is Pirelli’s biggest challenge. In this session, Pirelli shares how these systems are built using AWS and are made possible by modern observability tooling.

Breaking the monolith with style and speed (DOP206-S)

Microservices are here to stay, but nearly all of the most successful architectures originate from the classic monolith. The promised land of microservices is filled with treasures like decoupled deploys, scalability, resilience, development velocity, and more. However, the journey there can involve prolonged seasons of pain, suffering, and even regret. This talk is the story of how Stitch Fix used all three pillars of observability to build confidence, accelerate its migration, and collaborate with other teams. Learn about the strategies that Stitch Fix used and how it incorporated logs, metrics, and traces into these strategies.

Powering digital billboards with serverless (SVS209-S)

Digital billboards are everywhere from buildings to signs to transit stops. Place Exchange, a prominent auction platform for digital billboards, runs over 50,000 concurrent auctions 24/7 for placements on connected billboards in the world’s largest cities. In this talk, the Place Exchange team shares the challenges of managing, monitoring, and scaling a hybrid environment of edge devices all powered by a 100 percent serverless auction platform.

How Auto Scaling lets Braze efficiently send 2B+ messages per day (STP09)

Braze, a digital customer engagement platform, currently automatically scales more than 10,000 servers each week and relies on Amazon EC2 Auto Scaling groups to cost-effectively handle spikes in data and messaging traffic. In this talk, Braze’s CTO and co-founder, Jon Hyman, discusses Braze’s system architecture for managing Auto Scaling. This is a process that isolates Braze’s customer base into separate “clusters” that are each tied to multiple EC2 Auto Scaling groups. Hyman also shares some lessons learned as Braze has grown over the past eight years.

See you at AWS re:Invent

There are hundreds more sessions available, and these are just a few that we think you’ll find interesting. Take a look at the full schedule and you’re sure to find many more that pique your interest. And remember to stop by our booths in the Aria and the Venetian to say hello!