Refinery is Honeycomb’s sampling proxy, which our largest customers use to improve the value they get from their telemetry. It has a variety of interesting samplers to choose from. One category of these is called dynamic sampling. It’s basically a technique for adjusting sample rates to account for the volume of incoming data—but doing so in a way that rare events get more priority than common events. Honeycomb’s query engine can compensate for sampling rates on a per-event basis.

Observability is critical to innovation, but for many organizations, soaring costs make it impossible to achieve. Logz.io is here to change that.

We’ve posted before about how engineers on call at Honeycomb aren’t expected to do project work, and that whenever they’re not dealing with interruptions, they’re free to work on whatever will make the on-call experience better. However, all of our engineering rotations rely on hand-off meetings where they update the Slack groups with everyone who’s on call. During my last shift, a small problem kept causing friction for some of our incident management automation.

GenAI promises evolutionary changes in how we use observability tools, but meeting expectations means heeding the lessons of our AIOps mistakes. The emergence of generative AI in observability tools was inevitable, but there’s already been an extreme degree of hype in the market. Monitoring, DevOps and ITOps have never been immune to trends, and with GenAI capabilities, the propagandahype machine is running out of control.

Achieving modern observability with a unified data platform and Search AI If you have a love-hate relationship with your data, we don’t blame you. It’s generated at high velocity and from all sides — your apps, endpoints, networks, and servers. By 2025, global data creation is projected to grow by more than 180 zettabytes.* Inside this wealth of data lies better operational resilience, profitability, and innovation.

Prometheus is a go-to solution for monitoring containerized environments. This detailed guide will walk you through setting up Prometheus for Docker, helping you achieve robust monitoring for your containerized applications.

Change is the only constant in the IT landscape. These changes might involve adding new observability tools, retiring existing monitoring systems, establishing new business units, or integrating IT systems from acquisitions. Managing these changes can challenge even expert ITOps teams. Organizing your monitoring setup can seem overwhelming, especially with issues like monitoring gaps, observability redundancy, complex toolsets, or significant technical debt.

As organizations move their application workloads to the cloud, understanding the difference between cloud observability vs monitoring is crucial to ensure optimal performance and seamless operations. While both concepts are often mentioned in tandem, they serve different purposes, and mastering each can help organizations thrive in increasingly complex cloud environments.

It’s no secret that many businesses rely heavily on Amazon Web Services (AWS) for their infrastructure and application needs. While AWS offers scalability, flexibility, and reliability, managing and monitoring cloud resources can be challenging. That’s where AWS monitoring and observability can be a tremendous asset. Today, we will explore how implementing these practices is crucial for ensuring that your cloud environment operates smoothly, efficiently, and securely.

As our software complexity increases, so does our telemetry—and as our telemetry increases, it needs more and more tweaking en route to its final destination. You’ve likely needed to change an attribute, parse a log body, or touch up a metric before it landed in your backend of choice. At Honeycomb, we think the OpenTelemetry Collector is the perfect tool to handle data transformation in flight. The Collector can receive data, process it, and then export it wherever it needs to go.