Operations | Monitoring | ITSM | DevOps | Cloud

Disconnected pipelines. Unknown data sources. Costs that do not add up. Many teams struggle to answer a simple question. What data do we have and where is it going? In this clip, a Cribl customer explains how bringing all telemetry data together changed everything. With Cribl, their team can finally see what they collect, where it flows, and what it costs. That clarity unlocked smarter reduction, better routing decisions, and major optimization across security and observability workflows.

Customers asked for a simpler way to store and access telemetry data, and Cribl delivered. First came Cribl Lake. Cost effective data storage, flexible access, and identity based authorization instead of infrastructure based access rules. A simple way to retain data at rest and run slow, inexpensive analytics when needed. But the story did not end there.

In this episode of Pastries and SREs, our hosts tackle one of the hardest questions observability leaders face: "How do you prove the ROI of observability?" This isn’t just about uptime or dashboards. It’s also about aligning observability with business outcomes, cloud cost savings, and FinOps metrics that matter to leadership.

The revamped panel brings color highlighting, more flexible search, and an overall smoother experience to help you quickly find the “why” behind every log line. This is generally available in all editions of Grafana.

Good observability starts with good data. In this clip, we hear how Cribl gives teams real control over their data pipelines so they can collect, enrich, and route telemetry from any source to the right destination. It is not just about more dashboards or another platform. It is about building an observability ecosystem that connects IT, security, and the business through cleaner data and smarter AIOps. Tool rationalization and AI driven pipelines are not future goals. They are happening right now.

In this episode of Masters of Data, we dive into how AI learns, examining both how we teach it and what it derives from human performance, as well as why context plays a crucial role in AI interactions. We break down five key components of AI training and talk about why we should view AI as a tool under human control rather than an autonomous entity. We explore the challenge of maintaining context in AI—much like our own memory struggles—and discuss methods, such as retrieval-augmented generation, that can help AI retain context more effectively.