The open source community talks a lot about the problem of aligning incentives. If you’re not familiar with the discourse, most of this conversation so far has centered around the most classic model of open source: the solo unpaid developer who maintains a tiny but essential library that’s holding up half the internet. For example, Denis Pushkarev, the solo maintainer of popular JavaScript library core-js, announced that he can’t continue if not better compensated.

Last year, I wrote How We Define SRE Work. This article described how I came up with the charter for the SRE team, which we bootstrapped right around then. It’s been a while. The SRE team is now four engineers and a manager. We are involved in all sorts of things across the organization, across all sorts of spheres. We are embedded in teams and we handle training, vendor management, capacity planning, cluster updates, tooling, and so on.

Using Cribl Stream for observability is a given, but what about using Cribl Stream to get MORE from your data? Observability is all about being able to collect, route, store, and search your data. Implementing enrichment with observability provides more context and elevates your ho-hum data to robust information. This is key to faster, more confident decision-making!

JFrog’s new log analytics integration with New Relic brings together powerful observability capabilities to monitor, analyze, and visualize logs and metrics from self-hosted JFrog environments. The integration is free for all tiers of self-hosted JFrog customers and utilizes the powerful, open source log management tool, Fluentd, to collect, process, and surface data in New Relic dashboards.

In software observability, we often talk about three signal types - metrics, logs, and distributed traces. More recently I've been hearing about profiles as another signal type. In this article I will explain the different observability signals and when to use them in a clear and concise way.

Elastic Agent provides a new observability option for fully managed GKE clusters

This is the final blog of a three-part blog series on Observability—the challenges and the solutions.

Observability is a critical step for digital transformation and cloud journeys. Any enterprise building applications and delivering them to customers is on the hook to keep those applications running smoothly to ensure seamless digital experiences. To gain visibility into a system’s health and performance, there is no real alternative to observability. The stakes are high for getting observability right — poor digital experiences can damage reputations and prevent revenue generation.

It’s been a minute since our last Feature Focus, and we have a bit of catching up to do! I’m happy to report we’ll resume monthly updates next month, but until then, please enjoy this super-sized winter digest of what we’ve been up to at Honeycomb.

Today’s enterprises must have the capability to cope with the growing volumes of observability data, including metrics, logs, and traces. This data is a critical asset for IT operations, site reliability engineers (SREs), and security teams that are responsible for maintaining the performance and protection of data and infrastructure. As systems become more complex, the ability to effectively manage and analyze observability data becomes increasingly important.