In this guest article, SRE experts John Arundel and Andy Pearson show you how to connect your Checkly data to Prometheus and Grafana, as part of an integrated observability pipeline.

Grafana Labs cofounder and CEO Raj Dutt was a recent guest on the Designing Enterprise Platforms podcast from Early Adopter Research (EAR), speaking to host Dan Woods about the benefits of observability. The conversation touched on several related topics – including the tactics of observability, platform approaches, and why now is a great time to be part of an open source company.

To have Observability is to have the ability to understand your system’s internal state based on signals and externally-visible output. Honeycomb’s approach to Observability is to strive toward this: every feature of the product attempts to move closer to a unified vision of figuring out what your system did, and how it got there.

In the fast-moving world of software development, as your product and organization grow and evolve, there are almost always competing priorities. Zeroing in on what is most important to your business in order to take it to the next level can at times seem like a non-stop process of trial and error. Oftentimes the customer who screams the loudest becomes a priority and gets the most focus.

Oh man, did I get ahead of myself in my last post! I started chatting tools, and I realize now that I really should have been talking more about why I’m using Sensu and Gremlin. But it didn’t occur to me until last year at Monitorama. John Allspaw gave the keynote talk (Taking Human Performance Seriously). While you can watch the talk here, I’ll highlight a few points...

New eGuide takes a closer look at Prometheus, ELK and Jaeger:

Feature flags are great and serve us in so many ways. However, we do not love long-lived feature flags. They lead to more complicated code, and when we inevitably default them to be true for all our users, they lead to unused sections of code. In other words, tech debt. How do we stay on top of this? Find out how Honeycomb’s trigger alerts proactively tell you to go ahead and clean up that feature flag tech debt!

(This is the first post by our new head of Customer Success, Irving.) Sampling is a must for applications at scale; it’s a technique for reducing the burden on your infrastructure and telemetry systems by only keeping data on a statistical sample of requests rather than 100% of requests. Large systems may produce large volumes of similar requests which can be de-duplicated.

2020 is here and it looks like it’ll be a truly exciting and impactful year for the DevOps community. As you know, the landscape is changing rapidly, and as a result, new technologies and methodologies are emerging to solve challenges you’re experiencing on the job. Observability is one such concept–and achieving it is a huge challenge for software engineers across the globe.

We’re big fans of AWS Lambda at Honeycomb. As you may have read, we recently made some major improvements to our storage engine by leveraging Lambda to process more data in less time. Making a change to a complex system like our storage engine is daunting, but can be made less so with good instrumentation and tracing. For this project, that meant getting instrumentation out of Lambda and into Honeycomb.