make changes based on suggestions

alex-lin-msft · alex-lin-msft · commit d77c8f08d353 · 2022-12-30T16:14:01.000+08:00
diff --git a/articles/stream-analytics/cicd-overview.md b/articles/stream-analytics/cicd-overview.md
@@ -11,7 +11,7 @@ ms.date: 12/27/2022
 
 # Continuous integration and deployment (CI/CD) for Azure Stream Analytics
 
-You can build and deploy your Azure Stream Analytics job continuously using a source control integration. Source control integration creates a workflow in which any code updates would trigger a resource deployment to Azure. This article outlines the basic steps for creating a continuous integration and continuous delivery (CI/CD) pipeline.
+You can build and deploy your Azure Stream Analytics (ASA) job continuously 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.
 
 If you're new to Azure Stream Analytics, get started with the [Azure Stream Analytics quickstart](stream-analytics-quick-create-portal.md).
 
@@ -20,8 +20,10 @@ If you're new to Azure Stream Analytics, get started with the [Azure Stream Anal
 Follow the steps to create a CI/CD pipeline for your Stream Analytics project: 
 
 1. Create a Stream Analytics project using VSCode.
-
-   Use the ASA Tools extension for [Visual Studio Code](./quick-create-visual-studio-code.md) to [develop and test queries locally](develop-locally.md). You can also [export an existing job](visual-studio-code-explore-jobs.md#export-a-job-to-a-local-project) to your local machine.
+   
+    You can either create a new project or export an existing job to your computer using the ASA Tools extension for Visual Studio Code. Check out these two docs for more information:
+    * [Quickstart: Create a Stream Analytics job using VSCode](./quick-create-visual-studio-code.md) 
+    * [Export an existing job](visual-studio-code-explore-jobs.md#export-a-job-to-a-local-project)
 
 2. Commit your Stream Analytics project to your source control system, like a Git repository.
 
diff --git a/articles/stream-analytics/cicd-tools.md b/articles/stream-analytics/cicd-tools.md
@@ -11,7 +11,7 @@ ms.date: 12/27/2022
 
 # Automate builds, tests, and deployments of an Azure Stream Analytics job using CI/CD tools
 
-You can use the Azure Stream Analytics CI/CD npm package to automatically build, test, and deploy your Azure Stream Analytics projects using Visual Studio Code. You can create a new Stream Analytics project using the ASA Tools extension for Visual Studio Code or export an existing Stream Analytics job from Azure. This article describes how to use the npm package with any CI/CD system. For deployment with Azure Pipelines, see [Use Azure DevOps to create a CI/CD pipeline for a Stream Analytics job](set-up-cicd-pipeline.md).
+After developing your Stream Analytics project in Visual Studio Code, you can use the Azure Stream Analytics (ASA) CI/CD npm package to automatically build, test, and deploy your Stream Analytics projects. This article shows how to use the npm package with any CI/CD system. For deployment with Azure pipelines, see [Use Azure DevOps to create a CI/CD pipeline for a Stream Analytics job](set-up-cicd-pipeline.md).
 
 ## Installation
 
diff --git a/articles/stream-analytics/copy-job.md b/articles/stream-analytics/copy-job.md
@@ -12,7 +12,7 @@ ms.date: 12/27/2012
 
 # Copy, back up and move your Azure Stream Analytics jobs between regions
 
-If you want to move, copy or back up your Azure Stream Analytics jobs in Azure, the Azure Stream Analytics extension for Visual Studio Code allows you to export an existing job in the cloud to your local computer. All the configurations of your Stream Analytics job will be saved locally and you can resubmit it to another region.
+If you want to move, copy or back up your Azure Stream Analytics jobs in Azure, the Azure Stream Analytics extension for Visual Studio Code allows you to export an existing job in Azure cloud to your local computer. All the configurations of your Stream Analytics job will be saved locally and you can resubmit it to another cloud region.
 
 > [!NOTE] 
 > * Copying a job to another region does not copy the last output time. Therefore, you cannot use [**When last stopped**](./start-job.md#start-options) option when starting the copied job.
diff --git a/articles/stream-analytics/develop-locally.md b/articles/stream-analytics/develop-locally.md
@@ -10,7 +10,7 @@ ms.service: stream-analytics
 
 # Develop and debug Azure Stream Analytics jobs locally
 
-Apart from creating and developing an Azure Stream Analytics job in the Azure portal, you can set up a Stream Analytics job using a developer tool on your computer. Azure Stream Analytics (ASA) allows you to use your favorite code editor, e.g.,  Visual Studio Code to build and test query locally with live data streams from Azure Event Hubs, IoT Hub, Blob Storage and other Azure resources. The ASA developer tool creates a fully functional node in local runtime to run your Stream Analytics job. You can also submit your local Stream Analytics jobs to Azure and change the job running status. Using the ASA developer tool is a convenient way to build, test and manage your Stream Analytics jobs.
+Apart from creating and developing an Azure Stream Analytics job in the Azure portal, you can set up a Stream Analytics job using a developer tool on your computer. Azure Stream Analytics (ASA) allows you to use your favorite code editor, e.g.,  Visual Studio Code to build and test query locally with live data streams from Azure Event Hubs, IoT Hub, Blob Storage and other Azure resources. The ASA developer tool creates a fully functional node in local runtime to run your Stream Analytics job. You can also submit your local Stream Analytics jobs to Azure cloud and change the job running status. Using the ASA developer tool is a convenient way to build, test and manage your Stream Analytics jobs.
 
 ## Local development environments
 
@@ -20,7 +20,7 @@ The environments in the following table support local development:
 
 |Environment                              |Description    |
 |-----------------------------------------|------------|
-|[Visual Studio Code](visual-studio-code-explore-jobs.md)| The [Azure Stream Analytics Tools extension](https://marketplace.visualstudio.com/items?itemName=ms-bigdatatools.vscode-asa) for Visual Studio Code allows you to author, manage, test your Stream analytics job both locally and in the cloud with rich IntelliSense and native source control. Supports development on Linux, macOS and Windows. To learn more, see [Create an Azure Stream Analytics job in Visual Studio Code](quick-create-visual-studio-code.md). The extension also supports [Visual Studio Codespaces](https://visualstudio.microsoft.com/services/visual-studio-codespaces/), which is a cloud-hosted dev environment.|
+|[Visual Studio Code](visual-studio-code-explore-jobs.md)| The [Azure Stream Analytics tools extension](https://marketplace.visualstudio.com/items?itemName=ms-bigdatatools.vscode-asa) for Visual Studio Code allows you to author, manage, test your Stream Analytics job both locally and in the cloud with rich IntelliSense and native source control. It supports development on Linux, macOS and Windows. To learn more, see [Create an Azure Stream Analytics job in Visual Studio Code](quick-create-visual-studio-code.md). The extension also supports [Visual Studio Codespaces](https://visualstudio.microsoft.com/services/visual-studio-codespaces/), which is a cloud-hosted dev environment.|
 |[Command prompt or terminal](stream-analytics-tools-for-visual-studio-cicd.md)|The Azure Stream Analytics CI/CD NuGet package provides tools for Visual studio project build, local testing on an arbitrary machine. The Azure Stream Analytics CI/CD npm package provides tools for Visual Studio Code project builds (which generates an Azure Resource Manager template) on an arbitrary machine.|
 
 ## Next steps
diff --git a/articles/stream-analytics/optimize-query-using-job-diagram-simulator.md b/articles/stream-analytics/optimize-query-using-job-diagram-simulator.md
@@ -6,7 +6,7 @@ ms.author: zhenxilin
 ms.service: stream-analytics
 ms.custom: ignite-2022
 ms.topic: how-to
-ms.date: 10/12/2022
+ms.date: 12/27/2022
 ---
 
 # Optimize query using job diagram simulator
@@ -23,10 +23,17 @@ If you want to learn more about query parallelization, see [Leverage query paral
 
 ## How to use job diagram simulator?
 
-You can find the job diagram simulator in the Azure Stream Analytics tools extension for VSCode. If you haven't installed it yet, follow [this guide](quick-create-visual-studio-code.md) to install. 
+The **Job diagram simulator** feature simulates the job running topology in Azure and provides suggestions for adjusting Streaming Units. Therefore, you must configure live inputs and outputs for your Stream Analytics job and use them in the query. 
 
-In this tutorial, you learn to improve query performance based on edit suggestions and make it executed in parallel. As an example, we're using a non-parallel job that takes the input data from an event hub and sends the results to another event hub.
+Prerequisites: 
+* ASA Tools extension for VSCode. If you haven't installed it yet, follow [this guide](quick-create-visual-studio-code.md) to install. 
+* Configure live input and live output for your Stream Analytics job. 
+* You must include live input and output in the query.
+
+> [!NOTE]
+> The Job diagram simulator can't simulate job running topology for local inputs and outputs. It doesn't send results to the output destination.
 
+In this tutorial, you learn to improve query performance based on edit suggestions and make it executed in parallel. As an example, we're using a non-parallel job that takes the input data from an event hub and sends the results to another event hub.
 
 1. Open the Azure Stream Analytics project in VSCode after you finish your query authoring, go to the query file **\*.asaql** and select **Simulate job** to start the job diagram simulation.
 
