| title | description | services | author | ms.author | ms.service | ms.topic | ms.date |
|---|---|---|---|---|---|---|---|
Continuous integration and deployment for Azure Stream Analytics |
This article gives an overview of a continuous integration and deployment (CI/CD) pipeline for Azure Stream Analytics. |
stream-analytics |
su-jie |
sujie |
stream-analytics |
how-to |
9/22/2020 |
You can deploy your Azure Stream Analytics job continuously by using source control integration. Source control integration enables a workflow in which a code update triggers a resource deployment to Azure. This article outlines the basic steps for creating a continuous integration and deployment (CI/CD) pipeline.
If you're new to Azure Stream Analytics, get started with the Azure Stream Analytics quickstart.
Follow the steps in this guide to create a CI/CD pipeline for Stream Analytics.
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Develop an Azure Stream Analytics query.
Use Azure Stream Analytics tools for Visual Studio Code or Visual Studio to develop and test queries locally. You can also export an existing job to a local project.
[!NOTE] We strongly recommend using Stream Analytics tools for Visual Studio Code for best local development experience. There are known feature gaps in Stream Analytics tools for Visual Studio 2019 (version 2.6.3000.0) and it won't be improved going forward.
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Commit your Azure Stream Analytics projects 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.