Containers and microservices have revolutionized the way applications are deployed on the cloud. Since its launch in 2014, Kubernetes has become a standard tool for container orchestration. It provides a set of primitives to run resilient, distributed applications. One of the key difficulties that developers face is being able to focus more on the details of the code than the infrastructure for it. The serverless approach to computing can be an effective way to solve this problem.

In computing, virtualization is the creation of a virtual — as opposed to a physical — version of computer hardware platforms, storage devices, and network resources. Virtualization creates virtual resources from physical resources, like hard drives, central processing units (CPUs), and graphic processing units (GPUs). By virtualizing resources, you can combine a network of resources into what appears to users as one object.

Sleuth is pleased to announce a new option to start your Change Lead Time clock based on state transitions in your issue tracker! In our ongoing effort to meet customers where they are, we heard from many of you that you’d like Sleuth to account for and provide visibility into your pre-commit coding time. We’re pleased to offer this this new option to tell Sleuth which specific state transitions in your issue tracker should start your Change Lead Time clock!

Introducing project context restrictions for GitLab organizations. This feature enables project-based restrictions on contexts for standalone projects that are not tied to a VCS. Standalone projects are available at this time only with a GitLab integration with CircleCI. In this blog post, we hope to explain the value of this feature and how it can be used to further secure your workflows.

You might be excited about tracking DORA metrics, but have you ever thought about the ways in which you track them, and how important accuracy is in your methods?

We all know that observability is a must-have for operating systems in production. But we often neglect our own backyard — our software release process. We noticed we made that mistake here at Logz.io. We were wasting time and energy in handling failures in the CI/CD pipeline, and made our Developer-on-Duty (DoD) shifts tedious. That’s why it’s critical to incorporate your observability practices into your CI/CD pipeline.

Like I’ve mentioned in my last blog post, we use GitLab pipelines for packaging. We have a lot of software, like Icinga, Icingaweb and its various modules, which we want to build across multiple different operating systems. This results in a huge number of jobs and pipelines, doing very similar stuff. We have a lot of code repetition, and this is bad – code repetition means higher code maintenance , and it invites bugs.