Why do organizations invest in observability? Because it adds value. Sometimes we forget this when we’re building our observability solutions. We get so excited about what we’re tracking that we can lose sight of why we’re tracking it. Technical metrics reveal how systems react to change. What they don’t give is a picture of how change impacts the broader business goals. The importance of qualitative data in business observability is often overlooked.

A selection of live questions and answers from the audience of our recent webinar on how site reliability engineers can best leverage intelligent observability to monitor SLIs and SLOs, prioritize reliability over functionality, and more.

By 2022, Gartner estimates that more than 3 out of 4 global organizations will be running containerized applications in production. With this comes a new set of monitoring challenges — ephemeral, short-lived infrastructure, complex service interdependencies and on-call developers who now need access to data for fast troubleshooting, just to name a few.

Did you know that the VMware Tanzu Observability by Wavefront engineering team not only listens to input from customers, but acts on that input to improve the overall Tanzu Observability experience? In fact, as a direct result of customer surveys, the Wavefront engineering team recently completed 30 days of improvement focused on query quality. I will quickly run through those improvements in this post.

The concept of unified IT observability has gained newfound importance in today’s world of remote work, hybrid infrastructures, and technological convergence. In this video, Christina Kosmowski, President at LogicMonitor, shares five tips on how to power your business forward and achieve unified IT observability during uncertain times.

IT Operations teams are often the bedrock of the digital business, ensuring that processes and services continue humming smoothly as developers continue to evolve and increase customer value. But increasingly complex systems can flood them with alerts that get in the way of operators from doing their best work and paving the way for new, innovative services.

There is an idea of the relationship between observability and monitoring, that they complement each other in an inseparable way. While true that you can only monitor a system that is observable, the line dividing observability and monitoring grows narrower with every deployment you make; making these two practices less of a pairing and more a single entity.

Software companies are in a constant pursuit to optimize their delivery flow and increase release velocity. But as they get better at CI/CD in the spirit of “move fast and break things,” they are also being forced to have a very sobering conversation about “how do we fix all those things we’ve been breaking so fast?” As a result, today’s cloud-native world is fraught with production errors, and in dire need of observability.

Kafka is a distributed, highly available event streaming platform which can be run on bare metal, virtualized, containerized, or as a managed service. At its heart, Kafka is a publish/subscribe (or pub/sub) system, which provides a "broker" to dole out events. Publishers post events to topics, and consumers subscribe to topics. When a new event is sent to a topic, consumers that subscribe to the topic will receive a new event notification.

The current landscape of what our customers are dealing with in monitoring and observability can be a bit of a mess. For one thing, there are varying expectations and implementations when it comes to observability data. For another, most customers have to lean on a hodgepodge of tools that might blend open source and proprietary, require extensive onboarding as team members have to learn which tools are used for what, and have a steep learning curve in general.

After we’d fixed Aparna’s network issue, James came to see me at my desk. Masks on, socially distanced and all that, but it was nice to have some face-to-face time. James is cool – that dry British humor and not your classic IT Ops dude. He’s been here forever and mentored me when the CIO, Charlie, hired me as the first SRE here a year or so ago. I lucked out really.

Monitoring has always been a crucial operation in a software development cycle. This is mainly because of the complexity of industry-level IT and consumer-facing product development. Additionally, there is an ever-growing demand for rapid upgrades in products. To meet these requirements, streamlined performance and stability have become more important than ever; and without effective monitoring practices, they appear difficult to achieve.

Metrics measuring user engagement on your website are crucial for observability in marketing. Metrics will help marketing departments understand which of your web pages do not provide value for your business. Once known, developers can look at the web page’s technical metrics and determine if updates are required. Typically user engagement statistics, like the average time required to load your page, are stored separately from technical site logs.

Recently, we added a new derived column function to Honeycomb, INGEST_TIMESTAMP(), which can help customers debug event latency and/or inaccurate timestamps. A meaningful minority of the events sent to Honeycomb are already old when they arrive, and a very special few claim to have been sent from the future. Has this happened to you? Let’s do an experiment.

We are thrilled that Elastic has been recognized as a Leader and a Fast Mover in the 2021 GigaOm Radar Report for Cloud Observability. GigaOm Radar reports offer a forward-looking view of the market and are designed to help IT decision makers evaluate solutions with an eye to the future. Their analysts consider not just where the solutions are today, but also how the market is evolving and where the solutions are headed relative to that trend.

Last week, we announced that Refinery, our platform-agnostic sampling solution, is generally available. But it’s always better to see it for yourself. Follow the steps in this HoneyByte to see how Refinery works by running it locally.

Before we start, please take a moment to appreciate what day it is. IoT, or Internet of Things, has been a buzzword for longer than usual. Buzzwords usually have two common properties, and then their paths fork. I like thinking about buzzwords and about the useful aspects of what they mean. The most recent public example focuses on another buzzword currently in its hype phase: observability.

Digital transformation is accelerating at a staggering pace. Consider these statistics. In December 2019, Splunk partner Zoom had 10 million monthly active users. By the end of last year, that number was estimated to be closer to 300 million. It was part of an explosion of technological growth replicated across many industries and businesses in 2020. As Splunk CEO Doug Merritt said.

What precisely are the requirements of a DevOps practitioner, as opposed to an SRE, legacy developer, or operations manager? And do those specific requirements require a different approach to monitoring?

Amidst the nonstop pace of work to constantly evolve today’s digital business, we can forget to take a moment out to think about how it is that we’re doing that work. A new series of ‘coffee break’ webinars aim to provide that opportunity by pausing to look at the ways humans can best work with observability data. In particular, Coffee Break with Helen Beal looks at improving the work done by different types of software engineers that leverage artificial intelligence.

This is the second in a series of blog posts exploring the role that intelligent observability plays in the day-to-day life of smart teams. In this post, meet our clever ITOps engineer, James, as he reduces noise and distraction using intelligent observability.

I drive a 2005 Ford diesel pickup truck. Most of the time my truck runs great. But occasionally an orange light on the dashboard will flicker on to alert me that something is wrong. Unfortunately, there’s no information about what is wrong and why. My truck has a monitoring solution, but not an observability solution. In many cases, IT has the same problem as my truck.

All the work presented in this blog post is open source and available as part of our Splunk Connect for Ethereum repository examples, including the instrumentation of Besu as a Docker container, the configuration of Splunk, and two applications showing how to monitor Besu syncing to the chain.

As IT environments become more complex, enterprises running business-critical workloads in dynamic environments need to ensure the performance and reliability of their applications. This is where observability comes in. Observability is the ability of the internal states of a system to be inferred from external outputs. Without it, your team’s productivity could be greatly diminished.

Today, we announced that Refinery is now generally available. With Refinery, it’s now easy to highlight the critical debugging data you need and to stop paying for the rest. Refinery is a sampling solution that lets you control resource costs at scale without sacrificing data fidelity. Support for Refinery is now also included in Honeycomb Enterprise plans.