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Azure Pipelines

Score7 out of 10

17 Reviews and Ratings

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What is Azure Pipelines?

Users can automate builds and deployments with Azure Pipelines. Build, test, and deploy Node.js, Python, Java, PHP, Ruby, C/C++, .NET, Android, and iOS apps. Run in parallel on Linux, macOS, and Windows. Azure Pipelines can be purchased standalone, but it is also part of Azure DevOps Services agile development planning and CI/CD suite.

Categories & Use Cases

Reviews

What users are saying about Azure Pipelines.
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Azure Pipelines and fully integrated workflows

Incentivized
Matthew BudramView profile
Software Architect in Engineering at The future of banking (51-200 employees employees)

Use Cases and Deployment Scope

Azure Pipelines are used for any deployment of our apps, backend services and test automation. This is the backbone of our deployment process allows us to deliver within our release cycle. Our current deployment cycle is monthly - but at times we may have smaller more controlled deployments within a release cycle. Azure Pipelines are fully integrated with our workflows as we also use Azure DevOps and implementation was more effective and efficient for us than other tools that might exist.

Pros

  • Integration with SonarQube
  • Integration with Azure DevOps
  • Integration with GitHub

Cons

  • Error messaging when team members don't have permissions

Most Important Features

  • deployment
  • building
  • test coverage monitoring

Return on Investment

  • we have had outages from Azure in the past

Alternatives Considered

Jenkins and Travis CI

Other Software Used

Azure DevOps Services (formerly VSTS), Slack

Automates the CI/CD Pipeline

Incentivized
Verified User
Director in Information Technology (1-10 employees employees)

Use Cases and Deployment Scope

We were using the Azure Pipelines for CI/CD for our product deployment on Azure. We have integrated automated Pipeline associated with GitHub so whenever we merge any PR or commit code, that code becomes live in our frontend/backend in just few minutes. We have done integration via the Docker image.

Pros

  • Code Integration/Code Deployment
  • Azure Engine Auto Scaling Up with help of Pipeline
  • Managing Version Control and deploy in rollback with just one click

Cons

  • Integration of the Pipeline for new users

Most Important Features

  • Automated CI/CD
  • One time integration and runs life time
  • Build Deployment validation via tests

Return on Investment

  • Saved time in manual CI/CD
  • Generates the build according to set environment
  • Rollback specific version whenever you need old build

Alternatives Considered

GitHub and AWS CodePipeline

Other Software Used

GitHub, AWS IoT, Amazon Alexa for Business

Convenient tool to deploy changes to the production environment

Incentivized
Verified User
Engineer in Information Technology (10,001+ employees employees)

Use Cases and Deployment Scope

We're using Azure Pipelines for automating deploying our changes from the code in the repository to our production environment. We have a few pipelines for our separate environments. We're checking the quality of the changes that are going to be made and then if everything passes, we're doing a deployment.

Pros

  • all in one place
  • good ability to personalize deployment process
  • easy to develop

Cons

  • The errors which we got sometimes are not clearly enough.
  • There are some let's say hidden options, they could be more visible
  • When the process is running we have to remember about manually refreshing to see the current status because it doesn't work automatically

Most Important Features

  • Possibility to deploy changes automatically
  • You can write there scripts which will check your code
  • The repository and pipelines are in one place

Return on Investment

  • We don't have to deploy changes manually
  • But because of this that we automated some tasks, we need to be still aware of some edge cases we can meet and which can cause a pipelines failures
  • We can deploy changes pretty fast

Other Software Used

Visual Studio IDE, Microsoft Azure, Databricks Lakehouse Platform (Unified Analytics Platform)