Continuous integration (CI) and continuous delivery (CD), (or CI/CD), has become an integral part of software development and DevOps testing. It provides developers with the necessary features so that they can deploy the code continuously. It detects bugs at an early stage and avoids integration problems due to frequent committing of source code. With so many CI/CD tools available in the market, it becomes essential to choose the best CI/CD tools that suit the budget and project requirements. To make it easy for you, we have created this list, which we hope will help you choose the best CI/CD tool. Do you need to set up continuous integration in your project? The good news is that you’ve got options. 27 of the Best CI/CD Tools Available Today
ArgoCD is probably the most advanced GitOps tool we can use at this moment. It was featured in the Trial quadrant of the Thoughtworks Radar, May 2020 edition and it is also part of the CNCF Continuous Delivery Radar from June 2020 on Asses. The project was admitted into CNCF in April 2020 and since then it attracted more and more users and contributors.
fastlane is an open source library aimed to automised Android and iOS deployment but you can extend it and create awesome plugins to simplify your own and others development processes.
In this introduction to GitHub Actions, we’ll explore how to build and deploy a Python Flask application to AWS Elastic Beanstalk.
Over the past decade, continuous integration (CI) and continuous delivery (CD) have become staples of the software development lifecycle. CI automates the process of merging code and checking for basic regressions and code quality issues, relieving some of the code review burden on your dev team. CD and automated deployments eliminate the overhead involved each time a new feature or a hotfix needs to get deployed.
GitOps is a modern way to make better IaC for delivering apps in Kubernetes. It is all about determinism, idempotence, automation, observability… and many other exciting features! However, are you sure all this happens in the real world using existing approach and tools?
When it comes to GitOps efforts, amongst the many caveats and the varied snags to watch out for when configuring these, — is the DNS toil. I have been long procrastinating to get a running demo of this External-DNS https://github.com/kubernetes-incubator/external-dns for a little while, alas it is here now
Remember those days when CI/CD was something developers did because they hated manual, repetitive work? Fast-forward to now and CI/CD is an industry-wide best practice that’s on top of every IT manager’s agenda.
A quintessential piece for anyone working with distributed systems is the Fallacies of Distributed Computing by L Peter Deutsch. Even when working with modern platforms such as Kubernetes, the assertions made in the Fallacies of Distributed Computing prove to be very true around latency, bandwidth and system administration.
In most DevOps settings you’ll find that there are multiple environments in the pipeline. You might have conditions that change the environment based on which branch was merged or when a branch is tagged for release. There are a number of reasons you want to have more than just a production environment, the biggest reason being testing.
Argo CD is an open-source continues delivery tool for your project which runs on Kubernetes. Argo CD can be identified as an open-source continuous delivery tool for Kubernetes which has a graphical user interface to see Kubernetes components inside the cluster.
With time, the number of images in the container registry can grow substantially, taking up more and more space and costing you a fortune. In order to regulate, limit, or sustain acceptable grows rates in the registry space.
In the first part, I discussed some lessons I learnt from the “Continuous Delivery with Docker and Jenkins — Second edition” book (Chapters 1 to 5). It was about applying unit tests, code coverage tests, and acceptance tests for a Laravel 8 project (CI/CD). In that case, we used Amazon AWS, Jenkins, GitHub and Docker.
This article will look at how Continuous Delivery that has helped traditional software solve its deployment challenges be applied to Machine Learning.
Immutable infrastructure is like booting a machine from a virtual CD every time. When a piece of software or configuration changes, the CD gets thrown away and an updated disk loaded.
So as to get a handle on the quality of this cycle, it is important to perceive what DevOps represents. That’s why, in this article, I try to explain some of the most popular DevOps Myths.
In this article I will explain how to deploy a single page application to azure and use the “Static Website” feature of Azure Storage Accounts, also I will show you how to change some variables values in a JSON file during deployment.For this article I assume that you have already: An Azure (Portal) Account (and created a subscription and a resource group), An Azure DevOps Account (and a project configured).
This is a simple Continues deployment process that triggers the deployment on the required environment based on which directory you uploaded to S3 bucket using the following AWS tools: CloudFormation, CodeDeploy, Lambda, SAM, S3. I won’t also put the Slack (SNS, Lambda, Slack configs ) since there will be creating another post for that
Kubernetes is now the de-facto standard for container orchestration. With more and more organizations adopting Kubernetes. This post will focus on pushing out new releases of the application to our Kubernetes cluster i.e. Continuous Delivery.
Implement three different workflows to test, bump the version, and publish a new release. This tab allows you to add a workflow to your repository.