Integrates with Cycode

Azure Pipeline

Azure Pipeline is a cloud service used to build and test code automatically. Azure Pipeline includes continuous integration and continuous delivery (CI/CD) to regularly and consistently test and build code, and can deploy to any target.

Microsoft first released Azure in 2010 as a way for developers to deploy apps to the cloud. Developers can use Azure Pipelines with most application types, such as Java, JavaScript, Node.js, Python, .NET, C++, Go, PHP, and XCode. Azure Pipelines stands out in cases where your organization:

• Works with any language or platform
• Deploys to different types of targets at the same time
• Integrates with Azure deployments
• Builds on Windows, Linux, or Mac machines
• Integrates with GitHub
• Works with open-source projects

Developers using Azure benefit from applying the best practices, which include enforcing MFA and the principle of least privilege.

Azure Pipeline Terminology

Pipeline: a workflow that defines how our test, build, and deployment steps are run.

Stage: a logical boundary in the pipeline. It can be used to mark the separation of concerns. Each stage contains one or more jobs.

Job: represents an execution boundary of a set of steps. A stage can contain one or more jobs. Each job runs on an agent.

Step: the smallest building block of a pipeline. It can either be a script or a task. A task is simply an already created script offered as a convenience to you.

Agent: installable software that runs one job at a time.

Agent Pools: Group of agents managed collectively, as opposed to managing each agent individually.

Artifact: a collection of files or packages published by a run. The Artifact is made available to subsequent tasks, such as distribution or deployment.

Trigger: something set up to tell the pipeline when to run. We can configure a pipeline to run upon a push to the repository, at scheduled times, etc.

Environment: a collection of resources, where you deploy your application. It contains one or more virtual machines, containers, web apps, etc.

Checks: Checks define a set of validations required before a deployment can be performed.

Runs: It represents a single pipeline execution and collects the logs of running the steps and the results of running tests.