Operations | Monitoring | ITSM | DevOps | Cloud

Triaging an Incident with a Critical Data Pipeline at #rivian

Rivian makes electric vehicles to advance its mission to keep the world adventurous forever. As software defined vehicles, Rivian’s R1T and R1S are connected to the cloud from day 1, and telemetry data is at the heart of enabling mobile notifications, remote diagnostics, fleet management, and more. With so many critical pipelines in the cloud, observability is a top priority for the data platform.

Safely Roll Out Features with Datadog Feature Flags

In this short demo, see how Datadog Feature Flags help teams release new functionality safely and efficiently. Datadog provides advanced targeting, progressive rollouts, and automatic rollbacks — all integrated with powerful observability data. Learn how you can use simple on–off flags or multi-variant configurations to test and deploy features with confidence. With built-in monitoring of key guardrail metrics, Datadog can automatically pause or reverse rollouts when issues are detected, keeping your releases stable.

Building Smarter AI Products #Datadog #DASH #AI

AI capabilities are advancing faster than ever — transforming how teams design, build, and ship intelligent products. In this teaser from Building Successful AI-powered Products at Datadog DASH, experts discuss the rise of agent-based systems, evolving model capabilities, and how to stay ahead in the new era of automation.

How Datadog is Reinventing On-Call #Datadog #OnCall #DevOps

Datadog is reimagining how engineers handle incidents—moving beyond simple alerts to an intelligent, voice-driven on-call experience. With Datadog On-Call, teams can acknowledge alerts, access runbooks, post to Slack, and collaborate in real time, all before even touching their computer. See how Datadog brings incident response, communication, and automation together so you can respond faster and keep customers informed.

Monitor OCI spend, AI in DDSQL Editor, OTLP Metrics API, and more | This Month in Datadog

See how you can gain insights into cloud costs by tracking OCI spend and easily comparing instance types in October’s episode of This Month in Datadog. Join us for a spotlight of Cloud Cost Management’s support for Oracle Cloud Infrastructure, and the product’s new feature, Instance Explorer, which enables you to visualize and easily compare the cost and performance of instances across AWS, Azure, and Google Cloud.

Understand user experience through network performance with Datadog Synthetic Monitoring

When an application slows down or fails, pinpointing the cause isn’t always simple. Is it a backend regression, a misbehaving API, or a bottleneck somewhere deep in the network? Without full visibility, teams waste precious time troubleshooting across disconnected tools and layers. Datadog Synthetic Monitoring now supports Network Path to help you proactively identify whether user-facing issues stem from your code or from the underlying network.

Accelerate your Azure integration setup with guided onboarding

Getting started with monitoring for Microsoft Azure environments can be a lengthy and manual process. Many tools require users to create app registrations, assign permissions, and enable log forwarding or telemetry data collection across multiple portals and scripts. These fragmented steps slow down onboarding and introduce opportunities for misconfiguration, making it harder for teams to quickly achieve full visibility.

Store and search logs at petabyte scale in your own infrastructure with Datadog CloudPrem

As AI workloads and cloud-native applications expand, organizations are generating more log data than ever. Each service, container, and model inference produces continuous telemetry that must be stored, secured, and analyzed. As telemetry grows more complex, teams must balance full visibility with new retention and residency needs.

Automating your synthetic test infrastructure with Datadog Synthetic Monitoring and Terraform

Testing ecosystems contain massive amounts of data, including outlined test scenarios, prerequisite configurations, and the tests themselves. As a result, these ecosystems are prone to data sprawl. This makes it difficult to prevent configuration drift and quickly spin up new tests, especially at the frequency needed to support a fast-growing application. Teams can handle these challenges by treating their tests as part of their application infrastructure.