When you start building an observability stack, it’s natural to reach for logs first. They’re familiar, easy to generate, and often already part of a developer’s workflow. And sending logs to a centralized system feels like a quick win, too. Simply add a log shipper, and voila, your application is observable.

This meeting could’ve been an email. It’s a phrase most of us have said (or at least thought) at some point in our careers. For me, that realization hit years ago while working for a government organization. I’d frequently sit through long, agendaless meetings that seemingly went nowhere. I wasn’t sure why I was there. And because I’m an engineer at heart, I started to wonder: what were these meetings actually costing the organization?

In observability, we’ve traditionally been taught to think in terms of pillars, namely logs, metrics, and traces (and more recently, profiles). But pillars are rigid and disconnected. They don’t reflect how modern systems actually work or how we troubleshoot in real time. So let’s change that.

When you operate a restaurant, the last thing you want to do is shut your doors and turn away guests and staff because of some technology failure. And if you’re the one providing that tech, it’s your job to make sure that doesn’t happen. “For us, observability is about a lot more than just dashboards and alerts.

The benefits of observability are clear: deep visibility into system health, faster troubleshooting, and improved reliability (to name a few). But what’s equally clear is that, as organizations scale and evolve their observability strategies, they need a way to tap into these benefits without runaway costs. According to Grafana Labs’ 2025 Observability Survey, 74% of respondents say cost is a top priority for selecting tools.

Python developers often reach for Streamlit when they need to construct compelling web applications quickly. It provides a fast way to transform Python scripts into interactive applications without complex web frameworks. When paired with InfluxDB 3 Core, the leading time series database, engineers can build powerful real-time analytics dashboards entirely in Python.

In today’s fast-paced development environment, it’s critical that you understand what’s happening in your CI/CD pipeline. And yet, many teams struggle with fragmented tooling that makes it difficult to get a holistic view of their dev lifecycle. For example, if you’re using GitLab for CI/CD and Grafana for observability, you’ve probably faced this challenge: how do you bring your GitLab events into your existing observability and alerting infrastructure?