Operations | Monitoring | ITSM | DevOps | Cloud

In this episode of Masters of Data, we're breaking down AWS re:Invent 2025 through David's eyes (and probably a few cups of conference coffee). We dive into the massive crowds, killer customer conversations, and product demos that actually worked—because we're all about building real tech, not smoke-and-mirrors clickbait. David geeks out over Mobot, our AI tool that's making workflows smoother (not just another chatbot in disguise), and how attendees couldn't get enough of the live demos. We also throw some shade at the AI-washing epidemic and dig into why practical AI applications in security and observability actually matter.

🐧🐝 Learn what classic CPU metrics are (Load average, Node usage, and Container CPU usage) and why Delay Accounting can provide better, kernel-level insights into your system: https://t.ly/HQrWx

#DevOps #Kubernetes #AI #tech #observability #Linux #eBPF #Sysadmin #Cloud #Monitoring

Bindplane is a unified telemetry pipeline that helps teams cut observability spend by 50% or more. In this overview, you will learn how to route telemetry from any source to any destination, manage large fleets of OpenTelemetry Collectors, and gain real visibility into collector health, state, throughput, and routing behavior. 

Bindplane is introducing Fleets — a brand-new way to organize, manage, and operate large groups of OpenTelemetry Collectors at scale. In this video, Ryan walks through how Fleets simplify the way you group agents, roll out configuration changes, monitor health, and keep your entire collector fleet up to date.

Real-time alerts for your telemetry pipelines are here. In this quick overview, you’ll learn about the new Notifications panel in Bindplane. This update gives you real-time visibility into key changes across your configurations, fleets, and agents so nothing slips through the cracks. You’ll see how Notifications helps you stay ahead of: This new feature centralizes alerts you’d otherwise miss — making Bindplane easier to operate at scale. Email, Slack, and webhook notifications are also on the way.

Historically, Datadog has relied on technologies like Snowflake and Apache Spark on raw parquet files (lacking consistent table structure) to power internal analytics and data science at scale. As usage grew across product teams, more features depended on data science teams, and our datasets grew to include more telemetry data, these systems became complex to manage and govern both technically and financially. The need for a more flexible and scalable solution led Datadog to adopt Apache Iceberg, an open source table format for data lakes that brings reliability and performance while remaining SQL-friendly.