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Copy file name to clipboardExpand all lines: articles/stream-analytics/cicd-deploy-with-bicep.md
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title: Deploy Stream Analytics jobs with Bicep
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description: This article shows you how to generate and deploy Stream Analytics jobs using Bicep template.
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title: Deploy Azure Stream Analytics jobs using Bicep files
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description: This article shows you how to generate and deploy Azure Stream Analytics jobs using Bicep files.
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ms.service: stream-analytics
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author: alexlzx
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ms.author: zhenxilin
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ms.date: 05/24/2023
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ms.topic: how-to
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---
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# Deploy Stream Analytics jobs with Bicep
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# Deploy Stream Analytics jobs with Bicep files
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The Azure Stream Analytics (ASA) CI/CD npm package allows you to build, test, and deploy your Stream Analytics projects in your CI/CD pipeline using Bicep files. This article walks you through how to generate bicep files and deploy your ASA jobs.
title: Export and manage Stream Analytics jobs in Visual Studio Code
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title: Export and manage Azure Stream Analytics jobs in Visual Studio Code
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description: This article shows you how to export and manage an Azure Stream Analytics job with the Azure Stream Analytics extension for Visual Studio Code.
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ms.service: stream-analytics
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author: alexlzx
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# Export and manage Stream Analytics jobs in Visual Studio Code
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The Azure Stream Analytics extension (ASA) for Visual Studio Code gives you a seamless experience for managing Stream Analytics jobs in the cloud. It can be used across Windows, Mac and Linux platforms. With the ASA extension, it allows you to:
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The Azure Stream Analytics (ASA) extension for Visual Studio Code gives you a seamless experience for managing Stream Analytics jobs in the cloud. It can be used across Windows, Mac and Linux platforms. With the ASA extension, it allows you to:
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- Create, start, and stop a cloud job in Azure
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- Export existing jobs to a local machine
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1. Open your VS Code and select **Azure** icon on the activity bar. If you haven't installed the ASA extension, follow [this guide](./quick-create-visual-studio-code.md) to install.
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2. Select **STREAM ANALYTICS** in the explorer to locate the job you want to export.
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## List job and view job entities
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You can use the job view to interact with Azure Stream Analytics jobs from Visual Studio.
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1. Select the **Azure** icon on Visual Studio Code Activity Bar and then expand **Stream Analytics node**. Your jobs should appear under your subscriptions.
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2. You can interact with it and analyze your job with key metrics showing in chart. You can choose to view job-level metrics or nodes level metrics. And you can also decide which metrics you want them to show in the chart.
Copy file name to clipboardExpand all lines: articles/stream-analytics/visual-studio-code-local-run-live-input.md
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title: Testing Stream Analytics query locally against live stream input with Visual Studio Code
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title: Testing Azure Stream Analytics query locally against live stream input using Visual Studio Code extension.
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description: This article describes how to test query locally against live stream input using the Azure Stream Analytics Tools extension for Visual Studio Code.
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ms.service: stream-analytics
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ms.custom: build-2023
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## Define a live stream input
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1. Right-click the **Inputs** folder in your Stream Analytics project and select **ASA: Add Input**.
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OR press **Ctrl+Shift+P** to open the command palette and enter **ASA: Add Input**. Then choose the query script for processing the input stream.
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2. Choose a live input source type from the drop-down list, **Select from your Azure Subscriptions** and enter a name for the input source.
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3. You see a new JSON file created under the **Input** folder. Open the file and **Select from your subscriptions** to help you configure your Event Hubs input.
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4. Select **Preview data** to validate if the live input is successfully configured.
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## Run query locally
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Open your query file, select **Run locally** or the **Play** button on the menu bar to open a **Local run option** window for configuring local run mode.
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Choose **Use Live Input and Local Output** to run job locally against live input stream and produce output results to a local folder. Then select **Run** to start running job locally.
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You can see a **Local test** window is opened and refreshed every 3 seconds. The output results are stored as JSON format in **LocalRunOutputs** folder.
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## Specify input partitions for local testing
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2. Under **Select the input partitions** section and choose **Custom**.
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3. Enter the specific partition numbers that you want to ingest data from for your local testing.
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By specifying the partitions, you can focus on testing your query logic without worrying about a large volume of data. This feature allows for more efficient and targeted testing of your Stream Analytics job.
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2. In the submit options window, you can **update an existing job in Azure** or **create a new job** for the upload.
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3. Provide the necessary details, including your subscription, resource group, and region for the deployment.
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Once the submission process is complete, you can view your cloud job in a new window. From there, you can monitor its performance and manage its configurations as needed. You can also open the job in your browser by clicking on the link provided in the OUTPUT panel.
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