This video on Continuous Integration, Delivery, and Deployment, will help you understand the basics of these three primary concepts, Continuous Integration, Continuous Delivery, and Continuous Deployment. We shall learn about the features of each of them. Then we shall also see the relationship amongst the three concepts. Finally, we shall see why CI/CD is referred to as the best DevOps practice.
So, the topics we will be covering today are:
CI/CD pipelines have long played a major role in speeding up the development and deployment of cloud-native apps. Cloud services like AWS lend themselves to more agile deployment through the services they offer as well as approaches such as Infrastructure as Code. There is no shortage of tools to help you manage your CI/CD pipeline as well.
While the majority of development teams have streamlined their pipelines to take full advantage of cloud-native features, there is still so much that can be done to refine CI/CD even further. The entire pipeline can now be built as code and managed either via Git as a single source of truth or by using visual tools to help guide the process.
The entire process can be fully automated. Even better, it can be made serverless, which allows the CI/CD pipeline to operate with immense efficiency. Git branches can even be utilized as a base for multiple pipelines. Thanks to the three tools from Amazon; AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy, serverless CI/CD on the AWS cloud is now easy to set up.
#aws #aws codebuild #aws codecommit #aws codedeploy #cd #cd pipeline #ci #ci/cd processes #ci/cd workflow #serverless
A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials
In our previous article , we discussed the most common problems with Jenkins that made us search for an alternative. That’s why in this article, we’re offering a list of the most common Jenkins alternatives for continuous integration.
#uncategorized #ci/cd #ci/cd pipeline #continuous integration #gitlab ci #jenkins #jenkins alternatives
As a DevOps professional, you need to evaluate these tools based on your budget, project requirements, and other data points. This is why we take a deep dive into Travis CI vs Jenkinscomparison to help you decide the right CI/CD tool for your project requirements.
If you are new to DevOps and are just learning the basics then I recommend you to read this detailed article on Continuous Integration And Continuous Delivery. Without further ado, let’s get started.
Jenkins is a popular open-source CI/CD tool that is in usage for a long time. The tool is written entirely in Java. Jenkins has a powerful set of features that can be used to build, test, and integrate changes in a project.
It is the go-to choice for startups as it is free to use, supports a wide range of plugins, and is backed by a vibrant community. Developers get the chance to set up a CI/CD environment in Jenkins. Jenkins is available for a wide range of platforms – Windows, macOS, and various flavors of Unix (i.e. Ubuntu, OpenSUSE, and more).
Another major of Jenkins is its extensibility with plugins. Like other open-source projects, Jenkins maintains two release lines – weekly and LTS (Long Term Support). At the time of this article, the latest version of Jenkins (LTS) was 2.235.1.
#devops #continous delivery #jenkins ci #ci cd #travis ci #continous deployment #jenkins architecture
This post is part of a series that demonstrates a sample deployment pipeline with Jenkins, Docker, and Octopus:
In the previous blog post we used Octopus to build a Kubernetes cluster in AWS using EKS, and then deployed the Docker image created by Jenkins as a Kubernetes deployment and service.
However, we still don’t have a complete deployment pipeline solution, as Jenkins is not integrated with Octopus, leaving us to manually coordinate builds and deployments.
In this blog post, we’ll extend our Jenkins build to call Octopus and initiate a deployment when our Docker image has been pushed to Docker Hub. We will also create additional environments, and manage the release from a local development environment to the final production environment.
Octopus provides a plugin for Jenkins that exposes integration steps in both freestyle projects and pipeline scripts. This plugin is installed by navigating to Manage Jenkins ➜ Manage Plugins. From here you can search for “Octopus” and install the plugin.
The Octopus plugin uses the Octopus CLI to integrate with the Octopus Server. We can install the CLI manually on the agent, but for this example, we’ll use the Custom Tools plugin to download the Octopus CLI and push it to the agent:
We add the Octopus Server, our pipeline will connect with, by navigating to **Manage Jenkins ➜ Configure System **:
#java #tutorial #integration #docker #jenkins #ci/cd #jenkins pipeline #octopus