Datadog Summit

Datadog

We invite you to join us for the first ever Datadog Summit on October 26, 2016, in our hometown of New York City. The summit is a one-day event focused on you—our customers and community—followed by a cocktail reception at the Datadog offices.
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 leading companies such as Stripe and Airbnb are using Datadog to build a culture 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.
After the summit we’ll adjourn to Datadog headquarters in the New York Times building for cocktails, mingling, and sweeping views of the city below. 
<H2>Schedule</H2>
8:30 AM – 9:30 AM:  Registration and check-in
9:30 AM – 12:30 PM: Presentation sessions 
12:30 PM – 1:30 PM:  Lunch 
1:30 PM – 6:30 PM:  Hands-on training & Unconference discussion sessions
6:30 PM – 8:30 PM: Cocktails at Datadog HQ  (620 8th Avenue at 41st Street)
<H2>Presentations</H2>
Keynote: Alexis Le-Quoc (CTO, Datadog)
Keynote: Creating a culture of observability at Stripe with Datadog Speaker:  Cory Watson (Observability Engineer, Stripe) 
First Look: Full-stack observability with Datadog APM Speaker:  Matthew Perpick (Datadog) Traditionally, APM and infrastructure monitoring have been provided by separate tools.  With the release of Datadog APM, developers and operators now have a combined set of tools that provide visibility into their entire stack from cloud components to distributed tracing of custom applications.  Join Matt Perpick for a first look at the new infrastructure-aware Datadog APM offering.
Algorithimic Alerting - Detecting Abnormal Speaker: Homin Lee (Lead Data Scientist, Datadog) Monitoring even a modestly sized systems infrastructure quickly becomes untenable without automated alerting. For many metrics, it is nontrivial to define ahead of time what constitutes “normal” versus “abnormal” values. This is especially true for metrics whose baseline value fluctuates over time.  Join Datadog's Lead Data Scientist, Homin Lee, for an overview of the algorithimic alerting options in Datadog and how to use them to reduce noice, and increase the effectiveness of your alerting.
Datadog at Airbnb: Monitoring as code and other power user tips Speaker:  Ben Hughes (Airbnb) Airbnb has a unique ops culture that includes a volunteer pager duty rotation staffed by people from across engineering.  Good and accessible data is required for this scheme to work.  It becomes critical for alerts and pages to be relevant, informative, and actionable.  One technique that has been very successful for us has been working with alerts as code via our interferon project that integrates with the Datadog monitor API.  We also use new features from Datadog to improve our alerts and have some tricks that help us keep it all under control.
Monitoring Your Breakfast, Lunch and Dinner Deliveries at Grubhub  Speaker:  Jeff Valeo (GrubHub) The Cloud Infrastructure and SRE teams at Grubhub have worked to provide a standardized, automatic monitoring and alerting platform built on Datadog. This has allowed us to automatically create a suite of “baseline” monitors and alerts across all services. As new services are brought up in an environment, they are automatically discovered and baseline monitors and alerts are applied to the services. This means that there is no chance of “missing” a critical alert for a new service such as error rates, 500s, et cetera. We provide a simple way to implement the creation of new metrics in all services and then a simple DSL to ensure they are monitored and have proper alerting. SREs and Software Engineering work hand and hand to ensure all changes have metrics around them, and that anything important (or even remotely) is counted, timed and tracked. Additionally, we utilize the platform and thresholds to create the same alerts in both PreProduction and Production. This allows developers to know quickly if a change in a PreProd is unsuccessful out to Production.

YOTEL New York

We look forward to meeting you in October.

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