With the latest release of JFrog Artifactory, your Kubernetes world just got a lot bigger. Artifactory’s Docker registries are now compliant with the Open Container Initiative (OCI). Repository support for images compatible with OCI and support for the Helm 3 client mean you can run K8s with a high degree of versatility. Once you’re no longer locked exclusively into Docker, you can run either part of your container ecosystem or all of it using any OCI-compatible client.

Creating preview environments as a result of making pull requests is one of those practices that have vast potential and are yet largely overlooked. There is a strong chance that you are not using them, even though they can drastically increase productivity. I will not explain what preview environments are, besides stating that they are temporary environments created when pull requests are made and destroyed when PRs are closed.

Today marks our first step towards the future. Codefresh is launching a number of new features aimed at improving the experience and speed of continuous integration and deployment with GitOps.

We are excited to bring you our new and improved pull request experience. The entire Bitbucket engineering team has been enjoying the experience and are looking forward to you trying it. In the coming weeks, we'll begin to enroll more people into the new experience with the option to switch back to the old view if needed.

The Docker infrastructure abstracts a lot of aspects of the creation of images and running them as containers, which we usually do not know about nor interact with. One of those aspects is the handling of the filesystem inside the container. This post is a case study on how we discovered that writing large amounts of data inside a container has side effects with memory caching. Initially, we thought that we had an issue with our source code, but this was never the case.

One of our most commonly requested integrations, Datadog cloud monitoring, was announced last week on the Datadog blog! Sleuth organizes your deployments into projects, which collect and organize key data from your code sources and their associated staging environments. This data consists of metrics and errors.

CI/CD is a software development strategy which allows for faster development by introducing automation while still maintaining the quality of code deployed to production. Implementing a CI/CD pipeline not only promotes a safer deployment process but also improves the incident response process. CI/CD is broken down into multiple parts. The CI refers to continuous integration, meanwhile, the CD can refer to continuous delivery and/or continuous deployment.