With many blog posts published and talks given on the topic, it’s no secret that Datadog is running Kubernetes at scale. We currently run dozens of clusters, some of them with thousands of nodes. Additionally, we have clusters running in multiple clouds. How are we monitoring all of that, ensuring we can scale up quickly and safely?

Over 800 Engineers at Datadog do thousands of deployments per day, to hundreds of services in different environments, regions, and cloud providers. How can we manage all those deployments in a common way and have a reliable paper trail way to audit any changes?

Optimize and track user journeys with digital experience monitoring from Datadog.

Datadog is a monitoring and analytics platform that ingests trillions of data points per day, coming from more than 8,000 customers. With a complex distributed architecture and hundreds of deployments per day, needless to say sometimes things don't go as planned. Our teams have been improving the way incidents are managed at Datadog over the years and they are using that knowledge to help Datadog customers manage their own incidents.

Adam Savitzky, Senior Software Engineer at Carta discusses how Datadog’s unified platform enabled them to transition to microservices while reducing their application latency and deploying code faster.

In this video, you’ll learn how to monitor a system and its custom metrics efficiently using Datadog’s interconnected metrics, monitors, and logs.

Datadog offers a single unified platform to observe your infrastructure, applications, network flows, security threats, UX, and more. For full visibility, you can seamlessly navigate between metrics, traces, and logs. See inside any stack, any app, at any scale, anywhere.

Hear how Matt Hodge from Exclaimer leverages Datadog Log Management to migrate away from a homegrown solution and find one platform to manage dev and ops logs. Through deep integrations with Microsoft Azure, Exclaimer is able to gain rapid visibility into their entire Azure-based infrastructure as well.

Matthew Weber from StudySync discusses how quickly his team was able to ramp up on APM and get immediate insights with the context of a full observability platform.

In part 1 of this 4 part series, you’ll learn how to use manual instrumentation to add additional detail to traces. We’ll add new tags, or attributes, to the spans generated by our NodeJS application, allowing for more insightful data visualizations in App Analytics.