Operations | Monitoring | ITSM | DevOps | Cloud

Datadog Data Observability provides a complete solution with quality checks (e.g., volume, row changes, freshness), custom SQL-based monitors, anomaly detection, column-level lineage across systems like Snowflake and Tableau, full pipeline visibility, and targeted alerts when data issues arise.

Meet Datadog Instance Explorer — a way to explore, compare, and monitor cloud instance pricing and performance across AWS, Azure, and Google Cloud in one place. In this quick overview, you’ll learn how to: Start exploring your instance options today and make smarter, data-driven infrastructure decisions.

With Datadog GPU Monitoring, engineering and ML teams can monitor GPU fleet health across cloud, on-prem, and GPU-as-a-Service platforms like Coreweave and Lambda Labs. Real-time insights into allocation, utilization, and failure patterns make it easy to spot bottlenecks, eliminate idle GPU spend, and resolve provisioning gaps. By tying usage metrics directly to cost and surfacing hardware and networking issues impacting performance, Datadog helps teams make fast, cost-efficient decisions to keep AI workloads running reliably at scale.

While observability practices have evolved in recent years, they have largely focused on application services and infrastructure. Yet it is data what powers our applications, businesses, and AI models. When data issues occur, the consequences can be far reaching, from poor product experiences to billing errors to misinformed AI outcomes. In this session, Jonathan Morin, Group Product Manager at Datadog, shares real-world examples of incidents and explains how data observability can address them, helping teams detect issues earlier, reduce costly downtime, and restore trust in their data.

Fast front-end and back-end code alone won’t guarantee low end-to-end latency as hidden bottlenecks in the database can undermine even the best engineering efforts. In this session, Oleksii Serhiienko, Senior Site Reliability Engineer at GetYourGuide, will share how his team put database performance at the center of their monitoring strategy. He will highlight how they identified and fixed slow queries, uncovered load balancing issues that drove significant cost savings, and built monitoring practices that improved both reliability and investigation workflows.

When your logs don’t follow a standard format, it can be difficult to extract valuable information, like key-value pairs and nested JSON objects. Grok parsing lets you define flexible patterns that match unstructured log data so you can extract specific fields to query, filter, and visualize. In this video, you’ll learn how to: By refining your Grok parsers, you can make your logs more useful for analytics, dashboards, or alerts, and get even more value from your logs.