Over the last five years, the Datadog platform has grown. We added Application Performance Monitoring to complement our core infrastructure monitoring product, Log Management, Synthetic and Real User Monitoring, and more. For an enterprise software platform to be successful, the whole has to be greater than the sum of its parts. In Datadog’s case, this means users must be able to connect different types of data, pivot seamlessly from one context to another, and follow the thread of an investigation wherever it might lead.

Learn how end-to-end monitoring and observability enable enterprises to break down team silos and deliver industry-leading experiences for their customers and achieve business benefits such as: Improved business resilience by identifying and resolving IT risks faster before they result in customer service outages Increased competitive standing with DevOps and shift-left best practices to accelerate software releases.

Datadog, the observability platform used by thousands of companies, runs on dozens of self-managed Kubernetes clusters in a multi-cloud environment, adding up to tens of thousands of nodes, or hundreds of thousands of pods. This infrastructure is used by a wide variety of engineering teams at Datadog, with different feature and capacity needs.

Datadog, the observability platform used by thousands of companies, runs on dozens of self-managed Kubernetes clusters in a multi-cloud environment, adding up to tens of thousands of nodes, or hundreds of thousands of pods. This infrastructure is used by a wide variety of engineering teams at Datadog, with different feature and capacity needs.

Datadog’s ecosystem of 700+ integrations and Marketplace offerings enables you to see inside any source across your ever-growing tech stack. Discover today how Datadog’s ecosystem can help you consolidate your tools.

At Expedia Group, Kaushik Patel and Nikos Katirtzis have thousands of engineers and micro-services. Heterogeneity in terms of infrastructure and technologies used over the years created inefficiencies and posed the need for a set of automated best practices for our engineering teams. Over the past 2 years, using a data-driven approach, we’ve worked on creating a set of platforms that helps teams to adopt good reliability practices, including chaos engineering, release safety, or automatic failover between cloud regions. In this talk Kaushik and Nikos will cover the platforms they’ve built, including how they used data to drive their investment decisions.

To be successful, distributed teams require different strategies, processes, and rituals than co-located teams. Learn a variety of tips to build, nurture, and scale distributed teams across offices, home offices, timezones, countries, and cultures.

Hear from a Glovo engineer how Datadog Database Monitoring helped their storage team reduce costs and time spent on databases, as well as peak CPU usage.

Toyota is best known for making great cars and trucks, and as a leader in technology and mobility, they are on a mission to build a better future where everyone has the freedom to move. By partnering with Datadog, Toyota is taking advantage of the latest AI/ML to innovate and invent new ways for humans to be more mobile, while future proofing Toyota’s tech stack.

Processing data at scale involves moving packets through a network—but what happens when that network isn't cooperative? Anatole Beuzon, a Software Engineer at Datadog, discusses how he investigated and resolved network issues in Datadog’s larger data-processing apps and how you can apply these same methods to your own production workloads.