You probably use many tools to get you through the day. Do you ever wonder what tools get other people through their days? In our Tools This Engineer Uses series, we explore the routines, systems, and tools your peers rely on to solve problems and accomplish goals.

So you have a Grafana dashboard that shows failures at 0.01% and that latency is down throughout the company. But rather than get a pat on the back, “your boss’s boss is saying cut the crap or stop the mumbo jumbo. What does it really mean for our business?” said Peter Zaitsev, CEO of Percona, which offers solutions such as support, management services, consultant training, and custom engineering for MySQL, MariaDB, MongoDB, Postgres and other open source databases.

In our previous post about containers and microservices, we covered the challenges of monitoring these new technologies as well as how you can use software-defined IT operations tools to overcome them. The recently released Forrester Research report, Monitoring Containerized Microservices?

As industries shift to a microservices approach of deploying applications using containers, data scientists can reap the benefits. Data Scientists use specific frameworks and operating systems that can often conflict with the requirements of a production system. This has led to many clashes between IT and R&D departments. IT is not going to change the OS to meet the needs of a model that needs a specific framework that won’t run on RHEL 7.2.

We are happy to announce a new bugfix release for Icinga Web 2. Official packages are available on packages.icinga.com. Community repositories might need a while to catch up. You can find issues related to this release on our Roadmap.

It’s time to talk about the everyday challenges of serverless. Whenever I scroll through the latest blog posts on serverless it feels like there are only two types of problems in the field: how do I get started and how do I architect my solution. But what about all the day-to-day problems that developers and DevOps encounter when dealing with serverless? From simple deployment issues like protecting your stack from deletion to stress-testing your solution using serverless techniques.

If I’m used to metrics, how should I think about events in Honeycomb? This question cuts to the heart of how Honeycomb is different from other vendors in the APM and metrics space who claim to provide tools that help teams achieve observability, and we hear variations on it fairly often.

Last month, AWS announced an initiative called Open Distro for Elasticsearch. Since then, there has been a lot of debate as to what this move means for the future of the ELK Stack and the community. What are the motives behind this move? Does this split actually signify a fork?

Today we launched a new open source project called k3OS. K3OS is a Linux distro built for the sole purpose of running Kubernetes clusters. In fact, it is a Linux distro and the k3s Kubernetes distro in one! As soon as you boot up a k3OS node, you have Kubernetes up and running. When you boot up multiple k3OS nodes, they form a Kubernetes cluster. K3OS is perhaps the easiest way to stand up Kubernetes clusters on any server.

In this tutorial we are going to learn how to instrument Java code to expose application metrics using JMX monitoring. Following the code examples, you can monitor the availability, health and performance of your Java application. Java 1.5 introduced JMX – Java Management eXtensions – which is a standard way to instrument code in the JRE world.