One of the most important aspects of developing an efficient system is to have clean, helpful logs that will help you understand what errors are being triggered, and what information is being processed. When you are first creating an application, you might not know what logging framework will be most suitable for your future needs, or you could simply want your system to remain agnostic regarding the logging implementation to be used.

The team was busy speaking at events in Europe and the US this week, showing off new Grafana features and talking about what’s to come. Check out the presentation on logging in Grafana below; we’ll share the video once it’s available. Also in this week’s issue we have 2 new plugins to share, and your weekly dose of Grafana related blog articles and videos.

In this blog miniseries, I’d like to talk about how to think about doing data analysis “the Honeycomb way.” Welcome to part 1, where I cover what a heatmap is—and how using them can really level up your ability to understand what’s going on with distributed software. Heatmaps are a vital tool for software owners: if you’re going to look at a lot of data, then you need to be able to summarize it without losing detail.

When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.