by Datadog


Workshops will be announced on an ongoing basis. Stay tuned for more!

Workshops take place on July 16th. To ensure we are able to provide the best experience, you must reserve a seat in advance.

Datadog 101

Transform yourself from a monitoring novice to a Datadog expert with hands-on training led by the engineers who build, maintain, and support Datadog. We'll share best practices for building insightful dashboards and visualizations and tips for effective alerting and dive into container monitoring with Autodiscovery. Attendees will leave with hands-on experience using these techniques that they can bring home to their own environments for more effective monitoring.

Reducing MTTR with Log Management

Gaining insights into application behavior by combining metrics, tracing, and logs can help you reduce mean time to detection and resolution of operational incidents, allowing you to address issues before your users notice service impact. With the help of hands-on labs, this workshop will take attendees from beginner to expert in log management with Datadog. Participants will walk through best practices for log collection and processing, and then dive into scenarios that will build experience with troubleshooting and monitoring techniques. Attendees will leave with hands-on experience with logging, plus strategies that will provide insights into application behavior and user experiences.

Pinpointing Microservice Bottlenecks in Python with Datadog APM

As software moves to microservices and containers, the need for better tooling to debug our systems grows.In this workshop, we'll introduce distributed tracing as a method to gain visibility and insight into these distributed applications.Traces allow us to see units of work, as they pass across our subsystems. By incorporating traces with logs and metrics, we can see performance bottlenecks, verify legacy system changes, and deploy confidently into complex environments.We'll instrument a few Python microservices with the Datadog Agent, and see how distributed traces can be used in the real world, to get better insight into the health of your systems.

Creating Observable Applications in Go and Kubernetes

The greater move to Kubernetes is all about velocity.Shipping more features and more changes with greater confidence.In this workshop, we'll dive into adding to the confidence of deployment in complex environments, with APM and Distributed Traces.Combined with metrics and logs, our traces will allow us to update legacy systems, ship changes, and deploy in heterogeneous environments with confidence. We'll see how traces form the 'missing link' in getting the most out of your Kubernetes clusters.

Ensuring Reliability with SLOs

Uptime is a poor measure of reliability. Agile development’s fail-fast approach coupled with distributed applications and dynamic infrastructure requires us to have a better understanding of reliability.Service level objectives (SLOs) help you understand the true health of your systems and how your end users experience them. Poorly defined SLOs means you have little to no visibility into the successes and failures of those apps and their services. In this workshop you’ll learn how to define SLOs and monitor the right service level indicators to ensure reliability. Armed with this information, we'll introduce chaos into a sample application and learn how to respond effectively using error budgets.