April 16, 2019
Seattle, WA

We invite you to join us for Datadog Summit Seattle on April 16, 2019. The summit is a one-day event focused on you—our customers and community.

At the Datadog Summit, you will meet and learn from fellow community members, contributors, and Datadog staff. You'll get the latest product updates, learn how your fellow community members are using Datadog to build cultures of observability, and participate in open discussions and guided training sessions. In the hands-on training, we'll walk through best practices for building better dashboards and alerts, developing custom agent integrations, and monitoring and instrumenting your containerized applications.


Matt WilliamsDatadog
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Kirk KaiserDatadog
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Pierre GuceskiDatadog
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Daniel MaherDatadog
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Alex LandauRover
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More speakers to be announced soon...


Loews Hotel 1000,
1000 1st Ave,
Seattle, WA 98104


Open Spaces
Workshop: Intro to Datadog
Workshop: Hands on with Distributed Tracing and Datadog APM
Plenary Session
Open Spaces & Workshops
Workshop: Building a Datadog Integration
Workshop: Reducing MTTR with Log Management
Closing Session


Matt Williams

Technical Evangelist

Intro to Datadog

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.

Matt Williams is a Technical Evangelist on Datadog’s community team. He is passionate about the power of monitoring and metrics to make large-scale systems stable and manageable. So he tours the country speaking and writing about monitoring with Datadog. When he’s not on the road, he’s coding. You can find Matt on Twitter at @technovangelist.

Kirk Kaiser

Technical Evangelist

Hands on with Distributed Tracing and Datadog APM

Tracing is a specialized form of logging that is designed to work effectively in large, distributed environments. When done right, tracing follows the path of a request across process and service boundaries. This provides a big step up in application observability and can help inform a developer why certain requests are slow, or why they might have behaved unexpectedly. This tutorial will familiarize users with the benefits of tracing and describe a general toolkit for emitting traces from applications in a minimally intrusive way. We will walk through a simple example app, which receives an HTTP request, and gradually instrument it to be observable via traces. We will discuss language constructs that can generate traces --namely decorators, monkey-patching and context managers -- and give users pointers on how they might add tracing to their own applications and libraries. In the process, users will become familiar with the existing standards for modeling traces, and some of the challenges involved in adhering to this model in a distributed, asynchronous environment.

Kirk Kaiser is a Developer Evangelist at Datadog, where he focuses on helping developers build more reliable, performant code with APM. Previously, he was the lead backend developer at Triller, scaling the app from zero to millions of users, and handling the traffic from being in the top 100 of iOS and Android stores at the same time. Kirk is also the author of a book teaching Python programming through art instead of print statements, called Make Art with Python.

Pierre Guceski

Technical Writer

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.

Pierre is a technical writer with the Datadog Community team. He began his career as a technical evangelist at Logmatic.io. Through documentation and outreach, he strives to ensure the highest level of communication and understanding between products and people.

Daniel Maher

Technical Evangelist

Containers and Kubernetes Monitoring Best Practices

Containers and orchestration are the new normal, but along with these new paradigms comes increased complexity and new observability concerns. In this workshop, we'll explore some common containerization scenarios, and learn how to install, configure, and instrument those containers effectively with Datadog. Next, we'll launch a Kubernetes cluster and explore how those monitoring principles apply to orchestrated environments. Attendees will gain valuable hands-on experience with both Docker and Kubernetes, and how they integrate with the Datadog platform.

Dan is a long-time system administrator - he first installed on his PC in 1995 and never looked back. A veteran of the original dot com bubble, he founded a web hosting company in the late 90's, and has since worked in a variety of environments from start-ups to global corporations, including stints as a university lecturer and a day laborer. Today, Dan is a Technical Evangelist at Datadog, a role that allows him to satisfy his obsession with classifying and measuring things in general.

Alex Landau

Software Engineer

Alex Landau is passionate about creating scalable, robust software. He developed services for Amazon Marketplace which is used by millions of third-party sellers. He also worked on Amazon Search running load testing in preparation for Prime Day 2016. Prior to Amazon, Alex worked at Microsoft on the Application Insights team building an automated UI testing framework. He currently works at Rover where he drives platform performance, observability, and scaling efforts. Before graduating, Alex was a researcher at the University of Virginia where he worked with Wes Weimer's group on genetic algorithms to automatically find and fix bugs in code.