Operations | Monitoring | ITSM | DevOps | Cloud

Datadog

Transform Your Customer Experience with DevOps Collaboration

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.

Scaling Down Kubernetes Clusters

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.

Provisioning and Autoscaling

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.

Simplify customer support with Datadog's integrations for Zendesk

Zendesk provides support teams with an integrated solution for processing all types of customer inquiries and feedback. But as organizations scale, support tickets multiply, making it increasingly difficult to parse all of your customers’ feedback and time-consuming to investigate issues. Customers often report issues without providing the detailed context needed for troubleshooting, creating unclear and indirect paths to remediation.

Paving the Road for Proactive Reliability

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.

Detect Java code-level issues with Seagence and Datadog

In Java applications, concurrency issues can be difficult to reproduce and debug. Because work is scheduled nondeterministically across threads, the conditions that have led to an error in one execution of the program may not trigger the same issue the next time around. Exceptions that are silently handled—also known as swallowed exceptions—can also be challenging to debug because they typically do not leave any trace in the logs.

Quickly remediate issues in your Azure applications with Datadog Workflow Automation

Datadog Workflow Automation speeds up incident response and remediation for DevOps, SRE, and security teams by enabling them to automatically run predefined task sequences whenever specific alerts or security signals are triggered. After the feature’s initial release in 2023, Datadog is now excited to announce a significant expansion of its Workflow Automation capabilities with Azure actions, allowing engineers to create automated workflows for their Azure resources for the first time.