Alan Scherger discusses using Datadog to monitor HomeAway's development platform, which supports hundreds of engineers across the globe.

Container image security scanners are one of several tools we use in our development process to ensure the software that we ship to our customers is reliable and safe. In this talk, we’ll discuss our approach to continuous vulnerability monitoring (spoiler: it’s all automated), and how it increases our responsiveness while decreasing our operational cost.

In this session, Balthazar Rouberol discusses how he is using Kafka-Kit and Kubernetes to manage and scale the Kafka clusters that power Datadog's metric pipelines. Gwen Shapria (Confluent.io) also presents on how to deploy Kafka on K8S.

Rob Boll and Laurent Bernaille share their experiences in running and building the Kubernetes clusters that power Datadog.

Daniel Rieder, Senior Manager of Zendesk's Performance and Capacity Planning team, uses Datadog's Trace Search & Analytics to optimize capacity usage in Zendesk's hybrid infrastructure.

The Service Map automatically decomposes your application into all its services and dependencies so you can visualize your architecture in real time.

As our applications and services become more complex being able to visualize their interdependencies becomes key. In this session Ashley Miller and Willie Yao share how Datadog's Service Maps are helping Airbnb's team create context in an ever growing microservices based environment.

With anomaly detection, outlier detection, forecasting, and composite alerting, Datadog enables you to reliably alert the right people at the right time. But what happens when latency starts to increase, or error rates spike, in areas of your application where you haven’t set alerts? That’s what Watchdog is for.

Traditional logging solutions require teams to provision and pay for a daily volume of logs, which quickly becomes cost-prohibitive without some form of server-side or agent-level filtering. But filtering your logs before sending them inevitably leads to gaps in coverage, and often filters out valuable data.

Like any good e-commerce company, Wayfair collects a significant amount of data to use for business intelligence. Until recently, the majority of this data was crunched off-hours in preparation for business use the next day. We also create a great deal of data about our applications and infrastructure in real time.