title | description | author | ms.author | ms.service | ms.topic | ms.date |
---|---|---|---|---|---|---|
Continuous integration and continuous deployment of Azure Stream Analytics jobs |
This article gives an overview of setting up a continuous integration and deployment (CI/CD) pipeline for Azure Stream Analytics jobs. |
alexlzx |
zhenxilin |
stream-analytics |
how-to |
05/24/2023 |
You can build, test and deploy your Azure Stream Analytics (ASA) job using a source control integration. Source control integration creates a workflow in which updating code would trigger a resource deployment to Azure. This article outlines the basic steps for creating a continuous integration and continuous delivery (CI/CD) pipeline.
Follow the steps to create a CI/CD pipeline for your Stream Analytics project:
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Create a Stream Analytics project using VS Code. You can either create a new project or export an existing job to your local machine using the ASA Tools extension for Visual Studio Code.
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Commit your Stream Analytics project to your source control system, like a Git repository.
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Use Azure Stream Analytics CI/CD tools to build the projects and generate Azure Resource Manager templates for the deployment.
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Run automated script tests for quality regression.
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Deploy the job to Azure automatically.
You can use the command line and Azure Stream Analytics CI/CD tools to auto build, test, and deploy. You can also set up a CI/CD pipeline in Azure Pipelines. Azure Pipelines to enable more advanced capabilities, such as pipeline management, visualization, and triggers.