Jenkins is an extremely popular, open-source, continuous integration tool used for running tests, building code, and subsequently pushing to staging and then production. In a previous post, I outlined instructions for collecting, analyzing and visualizing Jenkins system logs.
As engineers, we are constantly bombarded with complex machine data. In order to better monitor and troubleshoot our environments, we must analyze and gain an accurate understanding of this data to best evaluate how our systems are performing and combat any issues that may occur. Yet, sifting through this data while managing the added intricacies of serverless architecture, microservices, containers, and other technologies make our jobs increasingly difficult to navigate.
Before I hop right in, it’s important to understand a bit about diabetes. Diabetes is what happens when your body cannot produce (type 1) or respond (type 2) to insulin effectively. The impact on the body is frequently quite severe — people who have difficulty controlling their blood sugar levels run the risk of losing feeling in their fingers and/or toes or even going into a coma if their blood sugar is either too high or too low.
A decade ago, an enterprise adopting DevOps was a rare event. In the last decade, some businesses dipped their toes in, others spent big money going all in. Now the question isn’t whether to make the culture shift, but how to be successful as it grows. It’s how to scale the adopted DevOps practices in the enterprise. This article is going to focus on highlighting common missteps and offer advice that can help you make decisions to handle the challenges ahead.
One of the biggest KPIs in the DevOps space is monitoring. There are so many tools to help any organization to complete their monitoring picture, but no tool does everything and most organizations use many tools to help complete their monitoring solution. Mashing tools together often creates a problem of its own — the tool sprawl problem.
It’s nearly the end of 2018 and we still discuss CI/CD and agile as separate concepts. The truth is, the line between them is blurring. Doing either or both well is very difficult. In fact, many organizations struggle to effectively execute an agile workflow, or reach CD because they are so difficult to do well. This article focuses on why it is so important to keep striving toward this gold standard duo because CI/CD and agile result in quality and predictability.
Elastic Stack 6.5 is out! Every new version of the Elastic Stack is packed with new features and updates, and as always, I’m happy to dive a bit deeper into the new release to provide our readers with a wrap up of what’s new. Interestingly enough, and as reflected in the announcements surrounding this release, this release is all about Kibana. That’s not to say the other components in the stack were left out – to the contrary, and I will cover them all, don’t you worry.
Let’s face it, Kibana and Grafana were naturally meant to go together, right? They’re both great individually, but sparks really start to fly when they work together! Each has their own strengths but combined they cover all the monitoring and troubleshooting use cases you need. So what is keeping these two highly compatible technologies apart? Nothing. Anymore.
Every year, we take time out of our everyday activities to join thousands of techies just like us for the biggest event of the season–AWS re:Invent. For one week at the end of November, industry leaders gather in Las Vegas to learn about new technologies from AWS as well as other partners in the cloud, DevOps, and IT spaces. The new technology, innovative ideas, and smart, ambitious people we have met over the past few years have been priceless for all of us at Logz.io.
Application Performance Monitoring, aka APM, is one of the most common methods used by engineers today to measure the availability, response times and behavior of applications and services. There are a variety of APM solutions in the market but if you’re familiar with the ELK Stack or are a Logz.io user, this article describes using a relatively new open source-based solution — Elastic APM.
