by Datadog

Workshops

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.

am sessions | 09:30 AM — 12:30 PM

Building a Datadog Integration

In this introductory-level workshop, attendees will design, build, and deploy a functioning Datadog integration. The goals are to learn about the design principles, configuration elements, and tooling for integrations, as well as to facilitate and encourage community members to create their own projects. Attendees should have basic familiarity with Python and git; however, this workshop is accessible to those who are relatively new to development.

GameDay with AWS

GameDay is a simulation exercise that challenges your AWS skills to deploy, maintain, and scale an application in the face of changes and threats from both inside and outside your organization. Participants are handed live AWS and Datadog accounts, and can expect a fully hands-on and gamified experience based off real-world events.

You'll work as part of a new DevOps team at Unicorn.Rentals, the company that dominates the Legendary Animal Rental Market (LARM). To maintain Unicorn.Rentals' market dominance, you will be helping us grow and scale our systems. You'll need to manage unpredictable feature releases, changes to our microservices architecture, traffic fluctuations, new requirements, and much more. You and your team will earn points by keeping your application running, the requests flowing, and your customers happy.

GameDay is suitable for anyone with some AWS experience: Whether you're a developer, solutions architect, or a DevOps practitioner, there's something for everyone. It's open-ended, so you can solve problems in a variety of ways. You'll have to communicate well with your team, prioritize your actions, and execute your plans to win the day, and some special prizes.

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.

Kubernetes Deep Dive: Catching and Preventing Failures

The Datadog engineering team has spent the last year running our production platforms and systems on Kubernetes. We have built up a wealth of knowledge about how it runs, how it fails, and how to troubleshoot all of the issues we never expected. The key to our success is great monitoring, and that includes getting tremendous insight from our logs.

In this interactive workshop, Datadog’s Compute Engineering team will show you how to use logs in conjunction with metrics to get full observability into your Kubernetes cluster. You’ll instrument a sandbox Kubernetes cluster and setup audit logging with Datadog. You will then use the Datadog Logs toolset to take action from raw data.

This session is ideal for anyone looking to accelerate their Kubernetes journey, and even advanced users can become more productive with their Kubernetes clusters.

pm sessions | 01:30 PM — 04:30 PM

Building a Datadog Integration

In this introductory-level workshop, attendees will design, build, and deploy a functioning Datadog integration. The goals are to learn about the design principles, configuration elements, and tooling for integrations, as well as to facilitate and encourage community members to create their own projects. Attendees should have basic familiarity with Python and git; however, this workshop is accessible to those who are relatively new to development.

GameDay with AWS

GameDay is a simulation exercise that challenges your AWS skills to deploy, maintain, and scale an application in the face of changes and threats from both inside and outside your organization. Participants are handed live AWS and Datadog accounts, and can expect a fully hands-on and gamified experience based off real-world events.

You'll work as part of a new DevOps team at Unicorn.Rentals, the company that dominates the Legendary Animal Rental Market (LARM). To maintain Unicorn.Rentals' market dominance, you will be helping us grow and scale our systems. You'll need to manage unpredictable feature releases, changes to our microservices architecture, traffic fluctuations, new requirements, and much more. You and your team will earn points by keeping your application running, the requests flowing, and your customers happy.

GameDay is suitable for anyone with some AWS experience: Whether you're a developer, solutions architect, or a DevOps practitioner, there's something for everyone. It's open-ended, so you can solve problems in a variety of ways. You'll have to communicate well with your team, prioritize your actions, and execute your plans to win the day, and some special prizes.

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.

Hands on with Chaos Engineering

Chaos Engineering involves running thoughtful, planned experiments that teach us how our systems behave in the face of failure. These experiments follow three steps: plan an experiment, contain the blast radius, scale or squash.

Tammy Butow (Gremlin) and Jason Yee (Datadog) lead a hands-on tutorial on chaos engineering, covering the tools and practices and metrics you need to implement chaos engineering in your organization. Even if you’re already using chaos engineering, you’ll learn to identify new ways to use chaos engineering within your engineering organization and discover how other companies are using chaos engineering—and the positive results they have had using chaos to create reliable distributed systems.

Getting up and Running with Serverless

Building your applications and services with Lambda allows your organization to move fast and focus on delivering value to your customers, by abstracting away infrastructure that doesn’t empower you to directly serve your customers better. But what do observability and operations for your applications look like when much of the infrastructure is abstracted away? In this course, designed for beginners to serverless, we’ll build a Lambda-based application on Node.js.

Attendees will learn how to automate serverless deployments with code, and gain observability into their infrastructure by combining metrics, traces, and logs in Datadog. They’ll leave this workshop with the confidence and tools they need to build their first production-ready serverless applications.

How to Efficiently Monitor Docker Enterprise Using Datadog

Docker Enterprise, the only independent, secure, enterprise-grade container platform to build, share, and run any application, anywhere. As enterprises face challenges on outages, security threats, governance, etc. successful integration of a monitoring and logging tool is key to healthy applications and infrastructure. Such a tool may be used to ensure both Docker Enterprise, and its hosted applications, are monitored effectively to either preemptively take corrective actions, or discover root cause post incident.

In this workshop sponsored by Docker, attendees will get hands-on experience to install and configure Docker Enterprise (including Docker Engine, Universal Control Plane and Docker Trusted Registry), an overview of Swarm and Kubernetes orchestration, deploying a sample application with both orchestrators, and monitoring the platform using Datadog.