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.

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.

Learn how Complyt is using Datadog Application Performance Monitoring (APM) and distributed tracing to turn data into knowledge and reduce application response times by more than 80%, which enabled them to meet SLAs for their largest customers.

What’s new at Datadog? An advanced feature to search and filter traces; measuring users who regularly engage with your app over time; and a centralized system for tracking, triaging, and addressing security issues.

Learn how the team at Complyt was able to integrate Cloud Cost Managament in a matter of hours and quickly pinpoint underutilized services to cut their cloud spend in half. CCM delivers cost data where engineers work and with resource-level context like CPU, memory, and requests — easily scoped to their services and applications — so that they can take action and spend effectively.