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This Edureka live video on “Build a CI CD Pipeline on Azure” will give you a brief introduction on how you can implement DevOps practices on Microsoft Azure.
Collaboration is a crucial element in software development; having the right collaboration tools can make a difference and boost the entire team’s productivity. Microsoft introduced its Application Lifecycle Management product with Team Foundation Server (aka TFS) on March 16th, 2006. This software had to be installed on a server within your network and had a user-based license. To reduce the complexity of setting up and maintaining the server, Microsoft released Visual Studio Online–an Azure-based, server-hosted version of TFS. Microsoft manages and administers the servers as well as taking care of backups. To clarify its commitment to agile and DevOps, Microsoft rebranded Visual Studio Online in 2015 as Visual Studio Team Services and later as Azure DevOps in 2018.
Since its beginning, this platform has changed significantly. For example, it introduced a customizable, task-based build service, release gates, and much more. Many organizations across the world made a significant investment to run their businesses on Azure DevOps. For this reason, after Microsoft announced the acquisition of GitHub in mid-2018, GitHub announced its automated workflow system, which is much like Azure Pipelines. It’s called GitHub Actions. Due to the switch, some companies became afraid of having to migrate their practices again. In the past few months, I have gotten several questions about whether it is still worth starting new projects on Azure DevOps, especially after the release of features like GitHub Advanced Security and GitHub Codespaces (similar to Visual Studio Codespaces). In this article, I’ll clarify the differences between these two platforms, and I’ll give you some advice on how you should be using them to your advantage.
To meet the needs of companies that want to keep their data within their network, both GitHub and Azure DevOps provide a server version of their platform. GitHub version is called GitHub Enterprise Server, and the Azure DevOps version is called Azure DevOps Server. Both versions require the client to install and maintain both software and machine.
On the other hand, there is a critical difference between their cloud-hosted version. While Azure DevOps Service allows you to choose the Azure region, which is closes to your organization’s location, to decrease the eventuality of networking latency during the creation of your organization (collection of projects). GitHub doesn’t provide this feature.
At the core of GitHub project management, we can find the issues. This task can be used to track any work item, from feature to bugs, and can be sorted into a Kanban-style board for easy consultation. The issue’s description also supports markdown syntax. Adding a specific keyword #issue-number (ex: #3) can associate the issue with another one. Each issue can be assigned to multiple developers, be linked to pull requests, and have various labels assigned to it. One can link a pull request to an issue to show that a fix is in progress and automatically close the issue when someone merges the pull request.
GitHub Kanban board
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Azure DevOps Tutorial for Beginners
Welcome to this Day-1 demo session on Introduction to Azure DevOps by Sandeep Soni (Azure DevOps Expert)
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The last couple of posts have been dealing with Release managed from the Releases area under Azure Pipelines. This week we are going to take what we were doing in that separate area of Azure DevOps and instead make it part of the YAML that currently builds our application. If you need some background on how the project got to this point check out the following posts.
The current setup we have uses a YAML based Azure Pipeline to build a couple of ASP.NET Core web applications. Then on the Release side, we have basically a dummy release that doesn’t actually do anything but served as a demo of how to configure a continuous deployment type release. The following is the current YAML for our Pipeline for reference.
name: $(SourceBranchName)_$(date:yyyyMMdd)$(rev:.r) resources: repositories: - repository: Shared name: Playground/Shared type: git ref: master #branch name trigger: none variables: buildConfiguration: 'Release' jobs: - job: WebApp1 displayName: 'Build WebApp1' pool: vmImage: 'ubuntu-latest' steps: - task: PowerShell@2 inputs: targetType: 'inline' script: 'Get-ChildItem -Path Env:\' - template: buildCoreWebProject.yml@Shared parameters: buildConFiguration: $(buildConfiguration) project: WebApp1.csproj artifactName: WebApp1 - job: WebApp2 displayName: 'Build WebApp2' condition: and(succeeded(), eq(variables['BuildWebApp2'], 'true')) pool: vmImage: 'ubuntu-latest' steps: - template: build.yml parameters: buildConFiguration: $(buildConfiguration) project: WebApp2.csproj artifactName: WebApp2 - job: DependentJob displayName: 'Build Dependent Job' pool: vmImage: 'ubuntu-latest' dependsOn: - WebApp1 - WebApp2 steps: - template: buildCoreWebProject.yml@Shared parameters: buildConFiguration: $(buildConfiguration) project: WebApp1.csproj artifactName: WebApp1Again - job: TagSources displayName: 'Tag Sources' pool: vmImage: 'ubuntu-latest' dependsOn: - WebApp1 - WebApp2 - DependentJob condition: | and ( eq(dependencies.WebApp1.result, 'Succeeded'), in(dependencies.WebApp2.result, 'Succeeded', 'Skipped'), in(dependencies.DependentJob.result, 'Succeeded', 'Skipped') ) steps: - checkout: self persistCredentials: true clean: true fetchDepth: 1 - task: PowerShell@2 inputs: targetType: 'inline' script: | $env:GIT_REDIRECT_STDERR` = '2>&1' $tag = "manual_$(Build.BuildNumber)".replace(' ', '_') git tag $tag Write-Host "Successfully created tag $tag" git push --tags Write-Host "Successfully pushed tag $tag" failOnStderr: false
#azure-pipelines #azure #azure-devops #devops
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As someone who has spent most of their (very short) career doing one click cloud resource deployments, I was shocked when I jumped onto a legacy project and realised the complexity of the deployment process to staging and production. Using a traditional .NET Framework application stack, the deployment process consisted of the following steps:
As you can see and may have experienced, this is a long, slow and error-prone process which can often take over an hour given likelihood of one of those steps not working correctly. For me it was also a real pain point having to use the client laptop, as it had 3 different passwords to get in, none of which I set or could remember. It also meant if we needed to do a deployment I had to be in the office to physically use the laptop — no working from home that day.
My first step was to automate the build process. If we could get Azure Pipelines to at least build the project, I could download the files and copy them over manually. There are plenty of guides online on how to set this up, but the final result meant it gave me a .zip artifact of all the files required for the project. This also took away a common hotspot for errors, which was building locally on my machine. This also meant regardless of who wrote the code, the build process was always identical.
The second step was to** set up a release pipeline**. Within Azure Pipelines, what we wanted to do was create a deployment group, and then register the server we want to deploy to as a target within that deployment group. This will allow us to deploy directly to an on premise server. So, how do we do this?
Deployment groups menu item in Azure DevOps > Pipelines
2. Create a new deployment group. The idea is you can have several servers that are in the same group and deploy the code to all of them simultaneously (for example for load balancing reasons). In my case I only have one target in my deployment group, so the idea of a group is a bit redundant.
#azure #azure-pipelines #deployment-pipelines #windows-server #azure-devops #devops