Operations | Monitoring | ITSM | DevOps | Cloud

In this video, you’ll learn how to scrape Prometheus endpoints with the OpenTelemetry Collector’s Prometheus receiver and send metrics to Splunk Observability Cloud. We’ll walk through configuring three common data sources (a Python Flask app, node_exporter for host metrics, and the NGINX Prometheus exporter), show how to enrich metrics with resource attributes, and build simple charts in Splunk Observability Cloud. You’ll see how centralized scraping and consistent tagging make it easy to manage and visualize Prometheus metrics in Splunk Observability Cloud.

You can give AI agents everywhere fingers & eyes into your tool or service, by implementing an MCP (Model Context Protocol) server. It’s a great idea! It’s also a new kind of design and engineering. Jessica describes how it’s different from implementing an API or a GUI, and why it’s more exciting than either.

Manual hunts for sensitive data are slow, error-prone, and expensive. Cribl Guard combines advanced AI with a human-in-the-loop control point to spot sensitive data, such as credit card, passport, and Social Security numbers, as it flows through Cribl Stream. Whether you’re fully cloud or hybrid, Cribl Guard puts you firmly in control of every piece of sensitive information that crosses your pipes.

LLM observability is broken There are too many new libraries floating around, but they don't follow accurately the OpenTelemetry conventions. OTel isn’t perfect for LLMs yet—but extending a proven standard beats inventing another one. Why not use the same standard (OTel) which works so well for rest of the apps, and just work on top of it? This is what I was ranting with Pranav Raj S, co-founder at Chatwoot and we thought there must be other folks facing similar issues.