Continuous integration and continuous deployment (CI/CD) of Stream Analytics jobs

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.

Create a CI/CD pipeline

Follow the steps to create a CI/CD pipeline for your Stream Analytics project:

1. 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.

   • Quickstart: Create a Stream Analytics job using VS Code
   • Export an existing job

2. Commit your Stream Analytics project to your source control system, like a Git repository.

3. Use Azure Stream Analytics CI/CD tools to build the projects and generate Azure Resource Manager templates for the deployment.

4. Run automated script tests for quality regression.

5. Deploy the job to Azure automatically.

Auto build, test, and deploy

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.

Next steps

• Automate builds, tests and deployments of an Azure Stream Analytics job using CI/CD tools
• Set up a CI/CD pipeline for Stream Analytics job using Azure Pipelines