Operations | Monitoring | ITSM | DevOps | Cloud

In this episode we'll visit the world of predictive analytics and machine learning and uncover how these cutting-edge technologies are transforming the way Datadog monitors and improves its services. We’ll focus our conversation on two key aspects: using advanced statistical methods for proactive monitoring and the strategic implementation of machine learning for algorithm enhancement.

This presentation outlines the various options for installing the Retrace Agent, with a focus on the manual installation process.

Datadog is an observability and security platform that ingests and processes tens of trillions of data points per day, coming from more than 22,000 customers. Processing that amount of data in a reasonable time stretches the limits of well known data engines like Apache Spark. In addition to scale, Datadog infrastructure is multi-cloud on Kubernetes and the data engineering platform is used by different engineering teams, so having a good set of abstractions to make running Spark jobs easier is critical.

APM tools promise a unified observability platform, yet they inherently focus on internal metrics, traces, and logs. This internal focus, while valuable, misses critical dimensions of the user experience and network performance that are essential for a complete understanding of application behavior in real-world scenarios. With Kentik you can dive deep into your network performance and find problems before they affect your users.