Operations | Monitoring | ITSM | DevOps | Cloud

Here at Grafana Labs, our commitment to our “big tent” philosophy runs deep. We prioritize interoperability and flexibility within our observability solutions, and believe you should be able to connect to and visualize data from a wide range of sources, including both open source and commercial technologies. Our rich ecosystem of Grafana data sources directly reflects these values — and today, we’re excited to share a recent milestone related to that ecosystem.

In the simplest of terms, container orchestration is the automated process of deploying, managing, scaling and networking containers. Containers are lightweight, portable self contained units that include an application or the processes needed to run applications. Docker is a great example of a project that helps to containerize or package applications, and was a large reason why containers gained such popularity around 2013. Before Docker there were Linux Containers (LXC).

What do the Snapchat, Airbnb, and Spotify iOS apps have in common? They all use order files to speed up their iOS app launch times. Order files re-order your binary to improve how symbols are loaded into memory. No code changes are necessary, but generating an optimized order file can be cumbersome, so it’s mostly done by larger teams or teams willing to pay for a service like Emerge Tools’ Launch Booster. It just so happens that Emerge Tools is now part of Sentry.

When troubleshooting with the Elastic Stack, Kibana is often the interface you’ll rely on to query and visualize logs. It doesn’t change the data—it just makes it searchable and a bit easier to work with under pressure. If you’re investigating an outage, tracking performance issues, or trying to correlate events across services, Kibana’s log exploration tools can speed up the process, assuming they’re configured and used well.

Many IT teams underestimate the true cost of managing infrastructure themselves. At first glance, DIY tools may seem like a cost-effective and flexible solution — but the workflows you build and manage with in-house tooling reveal a host of hidden expenses, inefficiencies, and risks as your IT scales. While it’s not a new problem, it’s one that’s revealing itself more and more clearly as time goes on.

Let’s face it: managing global data infrastructure isn’t just hard, it’s “I-just-deployed-the-wrong-config-to-prod-again” hard. If you’re a Cribl Admin or Operator working across clouds, continents, and compliance regimes, your to-do list probably reads like a series of increasingly desperate Post-it notes. Sources. Destinations. Pipelines. TLS settings. Proxies. Dev, staging, prod. Repeat. Forever. But what if we told you there’s a better way?

We’re excited to share that Icinga now offers an improved way to view dependencies. With the releases of Icinga DB Web 1.2.0, Icinga DB 1.4.0, and Icinga 2.15.0 today, any dependencies you’ve set up in Icinga will now be visually represented. Additionally, we’re introducing a new enterprise feature called Icinga Dependency Views, available through an Icinga subscription. This component expands Icinga DB Web with even more powerful capabilities.

On May 15th, 2025, Honeycomb hosted Observability Day (or O11yDay) in the London financial district. The skies were clear and the weather was wonderful and we had a huge turnout, from our networking breakfast to the happy hour at the end of the day.

I use Slack daily. It works perfectly fine. Outages rarely happen. Even if they happen, they are resolved quickly. And this is the same for many other tools. But how are they all doing it—Keeping services running and resolving issues quickly? The secret: On-Call Schedules. On-call schedules make sure someone is always available to handle emergencies, so your systems stay reliable.

We ran real-world load tests across seven different infrastructure plans—from Grid to Dedicated Split—using realistic conversion rates, bot traffic blends, and ERP-driven API imports. The findings were clear: performance scales predictably with resources, but only if your code, cache, and configuration keep up. This blog post walks through key results, why API load is disproportionately expensive, and what metrics matter most. How well does Shopware actually perform under load?