Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. To learn more about Datadog and start a free 14-day trial, visit Cloud Monitoring as a Service | Datadog. This month, we put the Spotlight on Bits AI for Incident Management.

Monitoring backend signals has been standard practice for years, and tech companies have been alerting their SRE and software engineers when API endpoints are failing. But when you’re alerted about a backend issue, it’s often your end users who are directly affected. Shouldn’t we observe and alert on this user experience issues early on? As frontend monitoring is a newer practice, companies often struggle to identify signals that can help them pinpoint user frustrations or performance problems.

In 2018 Datadog released Watchdog to proactively detect anomalies on your observability data. But what defines an anomaly? How do you avoid false positives? At Datadog Summit London 2024, Nils Bunge, product manager at Datadog, shared the story of the creation of the first Datadog AI feature (Watchdog Alert), what we learned from it and how we applied those lessons to all the added AI functionalities across the years.

On This Month in Datadog, we’re covering Datadog Security for Google Cloud, our integration with NVIDIA Triton Inference Server, and Sankey visualizations, which offer overviews of common paths users take across your app.

There are many different ways to implement Site Reliability Engineering (SRE). From team structures to roles and responsibilities to planning and prioritization flows, there’s no golden path for how to organize things. As Datadog has shifted from a startup to a quickly-growing public company, we’ve seen our own SRE practice evolve. With over 22,000 customers sending trillions of data points each day, keeping Datadog reliable is critical to our business.

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.