Operations | Monitoring | ITSM | DevOps | Cloud

IT is advancing blazingly fast. To keep up with architectural changes and hybrid environments, it’s more important than ever to maintain efficient infrastructure monitoring and troubleshooting. Adding to the complexity is the increase of distributed systems, comprised of many components and services.

Over the past few years, we’ve seen an almost obsession with developing and adopting CI/CD tools throughout the DevOps community. There are thousands of “how-to’s”, “top x tools”, and “tool x vs tool y” type articles, and it has gotten to the point where it’s quite difficult to figure out how and which one to pick as your own.

Everyone hates waiting in a queue. On the other hand, when you’re moving gigabytes of data around a cloud environment, message queues are your best friend. Enter Apache Kafka. Apache Kafka enables organisations to create message queues for large volumes of data. That’s about it – it does one simple but critical element of cloud-native strategies, really well.

At Elastic we are constantly innovating and releasing new features. As we release new features we are also working to make sure that they are tested, solid, and reliable — and sometimes we do find bugs or other issues. While testing a new feature we discovered a Linux kernel bug affecting SSD disks on certain Linux kernels. In this blog article we cover the story around the investigation and how it involved a great collaboration with two close partners, Google Cloud and Canonical.

Lately we’ve been working on improving different parts of the Mattermost server, including our monitoring and observability capabilities. We’ve been using Prometheus and Grafana to monitor our cluster for a while now, and you can read this great post where my colleague Stylianos explains how we have them working for our multi-cluster environment.

This blog is part of our series on how to use Atlassian features, automations, and integrations to take time back to ship better code, faster. Click here for the full list of 12+ new features or follow us on Twitter to stay up-to-date! Picture this: you've landed in a Development team that practices DevOps religiously (or maybe you're even leading one!). But despite spending hours integrating your tools and reading your edition of the Phoenix Project, you still feel like there's something missing.

This article will focus on using fluentd and ElasticSearch (ES) to log for Kubernetes (k8s). This article contains useful information about microservices architecture, containers, and logging. Additionally, we have shared code and concise explanations on how to implement it, so that you can use it when you start logging in your own apps. ‍Useful Terminology.

In this article we are going to consider the two most common methods for Autoscaling in EKS cluster: The Horizontal Pod Autoscaler or HPA is a Kubernetes component that automatically scales your service based on metrics such as CPU utilization or others, as defined through the Kubernetes metric server. The HPA scales the pods in either a deployment or replica set, and is implemented as a Kubernetes API resource and a controller.