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Gathering product requirements, doing user research, architecture discussions, optimizing internal workflows, researching new tools and developer productivity ideas, reviewing other teams’ diffs for impact, performance tuning, helping other engineers own & scale their code, fixing CI/CD pipelines. Fixing cron jobs, automating old setup docs, converting PXE/rsync to Chef/Puppet, converting Chef/Pup
In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is not implemented in other ways. So in this post, we're going to dive into the topic of time series databases (TSDBs) and why Honeycomb couldn't be limited to a TSDB implementation. If you've used a traditional metrics dashboard, you've used a time
Join Honeycomb at KubeCon + CloudNativeCon North America, November 6-9Meet with us 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: i
Make your costs predictable Unlike legacy metrics and monitoring tools, Honeycomb enables engineers to capture unlimited custom attributes for debugging with no impact on your spend. Simply pay by event volume—not number of seats, servers, or fields—solving the tradeoff between system visibility and cost. Resolve incidents faster Unlike tools that silo your data across three ‘pillars,’ Honeycomb u
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