Today we’re announcing a big move that will yield substantial, concrete benefits for our customers and for the entire DevOps community: A groundbreaking partnership with Docker that exempts Cloud users of the JFrog Platform from Docker Hub’s image-pull rate limits. This agreement further boosts JFrog’s vibrant ecosystem of integration partners, built on the “too integrated to fail” principle that’s been a pillar of our company since its founding.

Release management is a topic that leaders in DevOps teams should be concerned with as organizations move toward implementing systems of automated continuous deployment. The practice will make your organization more efficient, but how do you implement it? Modernizing your infrastructure for the cloud is essential to distributing trusted releases globally. Many enterprises choose AWS as their platform, and seek to use AWS edge services to distribute their production applications to nearby end-points.

Delivering modern applications is complicated and requires the coordination of many moving parts. Applications are frequently updated to implement new features and improve security and performance which translates to a better user experience for your customers. To further complicate matters, infrastructure must also be deployed and maintained simultaneously with applications to avoid conflicts or dependencies.

The adoption of cloud-based solutions has become increasingly common. The proof for this is evident – according to Gartner, Inc., the worldwide public cloud services market is expected to grow by 6.3% in 2020, up to a staggering $257.9 billion in value. The Flexera 2020 State of the Cloud Report, released on April 28, 2020, states that more than 90% of respondents have adopted cloud computing, with the top three cloud service providers being – AWS, Azure, and Google Cloud Platform.

In our previous guide, we documented 10 Docker anti-patterns. This guide has been very popular as it can help you in your first steps with container images. Creating container images for your application, however, is only half the story. You still need a way to deploy these containers in production, and the de facto solution for doing this is by using Kubernetes clusters. We soon realized that we must also create a similar guide for Kubernetes deployments.

This is the second part in our Kubernetes Anti-patterns series. See also part 1 for for the previous part and part 3 for the next part. You can also download all 3 parts in a PDF ebook.

This is the third and last part in our Kubernetes Anti-patterns series. See also part 1 and part 2 for the previous anti-patterns. You can also download all 3 parts in a PDF ebook.

I’ve recently started working on a new project to build a Discord bot in Go, mostly as a way to learn more Go but also so I can use it to manage various things in Azure and potentially elsewhere. I figured it’d be useful to document some of this project to give some insights as to what I’ve done and why. First up was setting up the CI/CD pipeline for it so that I don’t need to worry about it later and can save myself a bunch of time when testing.

Bitbucket Cloud’s pull request sidebar is useful because it displays all the relevant details of the pull request you’re looking at. However, with so much information present, it can be hard to find the information you need. To address feedback we’ve received about its readability and usability, we’ve given it a redesign.

Where do you usually track your code coverage? If you are not sure about the answer to this question or you would like to explore other options to the ones that you are currently using, then this post is for you. Specifically, this post details how you can use Codacy in your Codefresh pipeline to create and send coverage reports of your repository with every pipeline build. To follow along, make sure to have a Codacy and a Codefresh account. If not, now is the time to set-up a fresh account for free!