Operations | Monitoring | ITSM | DevOps | Cloud

A key factor in good package management is organization. How you organize and structure your repositories will help you to unlock the efficiencies and promises of modern DevOps processes. There are, of course, a multitude of ways you can organize packages. You can group by version numbers, formats, architectures, filetype and more.

ChartCenter is a free Helm chart central repository that was built to help the Helm community find immutable, secure, and reliable charts for publicly available Kubernetes apps. With ChartCenter, you have a single source of truth to proxy all public charts from one, always available location. Without ChartCenter, you need to add each Helm chart’s repository to the Helm client for every Kubernetes app you want to install and update.

Logs in continuous delivery pipelines are often entirely ignored, right up until something goes wrong. We usually find ourselves wishing we’d put some thought into our logs, once we’re in the midst of trawling through thousands of lines. In order to try to prevent this, we can add DevOps metrics into our logs, which will provide us with greater observability, and give insight into anything going wrong in our pipelines.

Nowadays, software development teams utilize continuous delivery or some variation, to create better, faster, more accurate software releases. Continuous delivery is a DevOps practice that empowers software teams to continuously ship code directly to an environment once automated tests pass. Continuous delivery is facilitated through the deployment pipeline. You can read more about it in a previous post.

GitLab CI/CD is a tool that is built into GitLab. It allows you to create automated tasks that you can use to form a Continuous Integration and Continuous Delivery / Deployment process. You configure GitLab CI/CD by adding a yaml file (called `.gitlab-ci.yml`) to your source repository. This file creates a pipeline, which will then run when a code change is pushed to the repository. Pipelines are made up of a series of stages, and each stage can each contain a number of jobs or scripts.

Developers understand the importance of keeping their team up to date on work and use many of the integrations between Bitbucket Cloud and Jira like smart commits and automation triggers to do so automatically. However there may be times where there are nuances or uncommon situations when merging a pull request that aren't covered by existing automation rules – sometimes there are multiple pull requests, or sometimes merging means you're now in 'Done' and other times you're in 'Releasing'.