The Datadog user community gathered in Austin on September 28 to learn about our newest features, hear how their peers are approaching monitoring and observability, and exchange ideas with the Datadog team and each other.
In talks and workshops, we introduced customers to a whole new Agent, comprehensive monitoring for containers, and an early look at our log product. We also heard from our customers, who related from the stage their own experiences with Datadog, whether in adopting microservices, monitoring processes, or developing new tools for monitoring automation.
Below is a sampling of what took place at Summit. For more, check out the full playlist of talks.
Agent 6.0 in beta
The just-released version of the Datadog Agent (currently in beta) is not only an improvement but a complete rewrite. Whereas previous Agent versions were written in Python, version 6.0 is written in Go. As software engineer Greg Meyer told the audience, the new code base gives the Agent a big boost in performance, with multi-threading, greater resource control, and lower overhead. And with the Python interpreter included in the Agent’s runtime, you can still use all of your existing custom Python checks. And, of course, the Agent is still open source.
Ever since Datadog acquired Logmatic.io to add log analytics to the Datadog platform, we’ve been excited to show what we’ve been working on. At Summit, director of product management Renaud Boutet introduced Datadog’s logging capabilities, explained how logs complement the rest of the platform, and showed how you can use logs to troubleshoot complex problems.
Advances in container monitoring
For Datadog users, deploying applications with Docker is more popular than ever. With Datadog’s Live Container view, which product manager Michael Gerstenhaber unveiled at Summit, we’re offering new tools for monitoring containers. Michael was joined on stage by Alan Scherger at HomeAway, who shared how his company has made use of the new features to gain global visibility into their containers and processes.
From monolith to microservices at Caviar
As Walter King showed, the food-delivery platform Caviar instrumented their applications with Datadog APM to gain insight into how user requests are executed in the real world. This helped Caviar maintain a focus on user experience as they split their application into microservices.
Our lead data scientist Homin Lee showed how to use Datadog to watch for anomalies and outliers, using algorithmic techniques to spot issues automatically. He also spoke about how machine learning algorithms can be used for predictive alerting.
More to see
At Summit, we enjoyed sharing our newest features and hearing about how our customers are using Datadog. In this post, we highlighted a few of the sessions from Summit, but there’s much more to learn. Check out the rest of the talks here.