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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
Recapping Datadog Summit Seattle 2019
Jeremy Garcia · 2019-05-08 · via Datadog | The Monitor blog

Last month, members of the Datadog community convened in Seattle for our customer summit. There, they discussed new developments in monitoring dynamic infrastructure and applications, learned about the latest updates to the Datadog platform, and shared tips, tools, and techniques from their own experiences.

This year, the Datadog Summit included a series of technical talks, hands-on workshops, and small-group Q&A sessions. We discussed new capabilities and language support for distributed tracing and APM; introduced automated browser tests; and debuted advanced logging features, like nested processing pipelines and automated correlation between logs and traces. In addition, Datadog users and partners shared their own diverse experiences, such as why application-level metrics remain important even as infrastructure technologies become increasingly advanced, and how to take the anxiety out of deploying new features.

For more, you can watch some of the product announcements and talks from the customer summit below, or see the full playlist here.

APM and distributed tracing

In this talk, Datadog product manager Priyanshi Gupta simulates a typical on-call workflow, pivoting between features like the Service Map, App Analytics, and flame graphs to detect and debug errors. In addition, Priyanshi announces some exciting additions to Datadog’s APM and distributed tracing offerings, from support for new languages (PHP and .NET) to our brand-new runtime metrics feature, which surfaces detailed data from your application’s runtime environment.

Advances in log management

Datadog product manager Stephen Lechner unveils a number of new log management features, including a way to unify logs and traces more tightly than ever. Datadog’s tracing libraries can now inject trace IDs into the logs generated while processing a request, automatically deep-linking log events and traces to provide instant, request-level context. In addition, Stephen also introduces the nested pipelines feature, which allows a team to independently organize and manage a number of parallel logs pipelines without having to worry about affecting their colleagues’ pipelines in the process.

How we made deploys less scary (Carta)

For developers and SREs, the feature release process is one of the most stressful parts of the job because of the potential for cascade effects. In this video, Adam Savitzky explains how Carta uses strategies and tools like feature flags, dark launches, and user bucketing to deliver new features to 700,000+ shareholders (and over 10,000 companies) while reducing the risk of introducing slowdowns or outages.

Application first, not infrastructure first (AWS)

Despite all the attention paid to changes in infrastructure, such as going from VMs to containers or instances to functions, many of the performance-monitoring fundamentals remain the same. In this talk, Abby Fuller of AWS shares some helpful tips and tools for monitoring important application-level metrics even as your infrastructure evolves. Towards that end, she also explains the critical role of a service mesh in facilitating application-level communications and networking, independent of whatever infrastructure platform you use.

Achieving huge performance wins with Datadog (Rover)

As a popular matchmaking service that connects pet care providers with owners, Rover relies on a small SRE team to support a complex application and a rapid release cycle. In this talk, Rover’s Alex Landau explains how the SRE team customized their Datadog dashboards, focusing on a selection of granular metrics in order to guide their performance monitoring and troubleshooting efforts. In addition, Alex and his team built an observability toolkit to empower developers to find and fix issues in pre-production, preventing smaller problems from escalating.

Tracking SLIs and SLOs

In order to improve visibility into the status of your SLOs and SLIs, Datadog recently released (in public beta) a new monitor uptime and SLO widget. As Datadog product manager Meghan Jordan explains, this new feature tracks your SLO performance over time, visualizes your remaining error budget, and helps you better understand where your performance stands in relation to your SLOs.

We’re grateful to everyone who shared their wisdom at Summit, and we hope that you find the talks engaging and informative. If you’d like to see more, please take a look at our full playlist of videos here.