freshness

Author: mamccrea

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/configure-stream-analytics-output.png

@@ -4,12 +4,12 @@ description: This article describe the concept of inputs in an Azure Stream Anal
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 04/25/2018
+ms.date: 06/11/2019
 ---
+
 # Understand inputs for Azure Stream Analytics
 
 Azure Stream Analytics jobs connect to one or more data inputs. Each input defines a connection to an existing data source. Stream Analytics accepts data incoming from several kinds of event sources including Event Hubs, IoT Hub, and Blob storage. The inputs are referenced by name in the streaming SQL query that you write for each job. In the query, you can join multiple inputs to blend data or compare streaming data with a lookup to reference data, and pass the results to outputs. 

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/create-pbi-ouptut-with-dataset-table.png

@@ -7,17 +7,19 @@ ms.author: mamccrea
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 12/06/2018
-ms.custom: seodec18
+ms.date: 06/11/2019
 ---
 # Get started with Azure Stream Analytics to process data from IoT devices
-In this tutorial, you will learn how to create stream-processing logic to gather data from Internet of Things (IoT) devices. We will use a real-world, Internet of Things (IoT) use case to demonstrate how to build your solution quickly and economically.
+
+In this tutorial, you learn how to create stream-processing logic to gather data from Internet of Things (IoT) devices. We will use a real-world, Internet of Things (IoT) use case to demonstrate how to build your solution quickly and economically.
 
 ## Prerequisites
+
 * [Azure subscription](https://azure.microsoft.com/pricing/free-trial/)
 * Sample query and data files downloadable from [GitHub](https://aka.ms/azure-stream-analytics-get-started-iot)
 
 ## Scenario
+
 Contoso, which is a company in the industrial automation space, has completely automated its manufacturing process. The machinery in this plant has sensors that are capable of emitting streams of data in real time. In this scenario, a production floor manager wants to have real-time insights from the sensor data to look for patterns and take actions on them. We will use the Stream Analytics Query Language (SAQL) over the sensor data to find interesting patterns from the incoming stream of data.
 
 Here data is being generated from a Texas Instruments sensor tag device. The payload of the data is in JSON format and looks like the following:

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/create-power-bi-ouptut.png

@@ -7,10 +7,10 @@ ms.author: jeanb
 ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 12/07/2018
-ms.custom: seodec18
+ms.date: 06/11/2019
 ---
-# Tutorial: Stream Analytics and Power BI: A real-time analytics dashboard for streaming data
+# Stream Analytics and Power BI: A real-time analytics dashboard for streaming data
+
 Azure Stream Analytics enables you to take advantage of one of the leading business intelligence tools, [Microsoft Power BI](https://powerbi.com/). In this article, you learn how create business intelligence tools by using Power BI as an output for your Azure Stream Analytics jobs. You also learn how to create and use a real-time dashboard.
 
 This article continues from the Stream Analytics [real-time fraud detection](stream-analytics-real-time-fraud-detection.md) tutorial. It builds on the workflow created in that tutorial and adds a Power BI output so that you can visualize fraudulent phone calls that are detected by a Streaming Analytics job. 
@@ -32,41 +32,31 @@ In the real-time fraud detection tutorial, the output is sent to Azure Blob stor
 
 1. In the Azure portal, open the Streaming Analytics job that you created earlier. If you used the suggested name, the job is named `sa_frauddetection_job_demo`.
 
-2. Select the **Outputs** box in the middle of the job dashboard and then select **+ Add**.
-
-3. For **Output Alias**, enter `CallStream-PowerBI`. You can use a different name. If you do, make a note of it, because you need the name later. 
-
-4. Under **Sink**, select **Power BI**.
-
-   ![Create an output for Power BI](./media/stream-analytics-power-bi-dashboard/create-power-bi-ouptut.png)
-
-5. Click **Authorize**.
+2. On the left menu, select **Outputs** under **Job topology**. Then, select **+ Add** and choose **Power BI** from the dropdown menu.
 
-    A window opens where you can provide your Azure credentials for a work or school account. 
+3. Select **+ Add** > **Power BI**. Then fill the form with the following details and select **Authorize**:
 
-    ![Enter credentials for access to Power BI](./media/stream-analytics-power-bi-dashboard/power-bi-authorization-credentials.png)
+   |**Setting**  |**Suggested value**  |
+   |---------|---------|
+   |Output alias  |  CallStream-PowerBI  |
+   |Dataset name  |   sa-dataset  |
+   |Table name |  fraudulent-calls  |
 
-6. Enter your credentials. Be aware then when you enter your credentials, you're also giving permission to the Streaming Analytics job to access your Power BI area.
+   ![Configure Stream Analytics output](media/stream-analytics-power-bi-dashboard/configure-stream-analytics-output.png)
 
-7. When you're returned to the **New output** blade, enter the following information:
+   > [!WARNING]
+   > If Power BI has a dataset and table that have the same names as the ones that you specify in the Stream Analytics job, the existing ones are overwritten.
+   > We recommend that you do not explicitly create this dataset and table in your Power BI account. They are automatically created when you start your Stream Analytics job and the job starts pumping output into Power BI. If your job query doesn't return any results, the dataset and table are not  created.
+   >
 
-   * **Group Workspace**: Select a workspace in your Power BI tenant where you want to create the dataset.
-   * **Dataset Name**:  Enter `sa-dataset`. You can use a different name. If you do, make a note of it for later.
-   * **Table Name**: Enter `fraudulent-calls`. Currently, Power BI output from Stream Analytics jobs can have only one table in a dataset.
-
-     ![Power BI workspace dataset and table](./media/stream-analytics-power-bi-dashboard/create-pbi-ouptut-with-dataset-table.png)
-
-     > [!WARNING]
-     > If Power BI has a dataset and table that have the same names as the ones that you specify in the Stream Analytics job, the existing ones are overwritten.
-     > We recommend that you do not explicitly create this dataset and table in your Power BI account. They are automatically created when you start your Stream Analytics job and the job starts pumping output into Power BI. If your job query doesn't return any results, the dataset and table are not  created.
-     >
+4. When you select **Authorize**, a pop-up window opens and you are asked to provide credentials to authenticate to your Power BI account. Once the authorization is successful, **Save** the settings.
 
 8. Click **Create**.
 
 The dataset is created with the following settings:
 
-* **defaultRetentionPolicy: BasicFIFO**: Data is FIFO, with a maximum of 200,000 rows.
-* **defaultMode: pushStreaming**: The dataset supports both streaming tiles and traditional report-based visuals (a.k.a. push).
+* **defaultRetentionPolicy: BasicFIFO** - Data is FIFO, with a maximum of 200,000 rows.
+* **defaultMode: pushStreaming** - The dataset supports both streaming tiles and traditional report-based visuals (a.k.a. push).
 
 Currently, you can't create datasets with other flags.
 
@@ -84,54 +74,52 @@ For more information about Power BI datasets, see the [Power BI REST API](https:
     >[!NOTE]
     >If you did not name the input `CallStream` in the fraud-detection tutorial, substitute your name for `CallStream` in the **FROM** and **JOIN** clauses in the query.
 
-        ```SQL
-        /* Our criteria for fraud:
-        Calls made from the same caller to two phone switches in different locations (for example, Australia and Europe) within five seconds */
+   ```SQL
+   /* Our criteria for fraud:
+   Calls made from the same caller to two phone switches in different locations (for example, Australia and Europe) within five seconds */
 
-        SELECT System.Timestamp AS WindowEnd, COUNT(*) AS FraudulentCalls
-        INTO "CallStream-PowerBI"
-        FROM "CallStream" CS1 TIMESTAMP BY CallRecTime
-        JOIN "CallStream" CS2 TIMESTAMP BY CallRecTime
+   SELECT System.Timestamp AS WindowEnd, COUNT(*) AS FraudulentCalls
+   INTO "CallStream-PowerBI"
+   FROM "CallStream" CS1 TIMESTAMP BY CallRecTime
+   JOIN "CallStream" CS2 TIMESTAMP BY CallRecTime
 
-        /* Where the caller is the same, as indicated by IMSI (International Mobile Subscriber Identity) */
-        ON CS1.CallingIMSI = CS2.CallingIMSI
+   /* Where the caller is the same, as indicated by IMSI (International Mobile Subscriber Identity) */
+   ON CS1.CallingIMSI = CS2.CallingIMSI
 
-        /* ...and date between CS1 and CS2 is between one and five seconds */
-        AND DATEDIFF(ss, CS1, CS2) BETWEEN 1 AND 5
+   /* ...and date between CS1 and CS2 is between one and five seconds */
+   AND DATEDIFF(ss, CS1, CS2) BETWEEN 1 AND 5
 
-        /* Where the switch location is different */
-        WHERE CS1.SwitchNum != CS2.SwitchNum
-        GROUP BY TumblingWindow(Duration(second, 1))
-        ```
+   /* Where the switch location is different */
+   WHERE CS1.SwitchNum != CS2.SwitchNum
+   GROUP BY TumblingWindow(Duration(second, 1))
+   ```
 
 4. Click **Save**.
 
 
 ## Test the query
+
 This section is optional, but recommended. 
 
 1. If the TelcoStreaming app is not currently running, start it by following these steps:
 
-    * Open a command window.
+    * Open Command Prompt.
     * Go to the folder where the telcogenerator.exe and modified telcodatagen.exe.config files are.
     * Run the following command:
 
        `telcodatagen.exe 1000 .2 2`
 
-2. In the **Query** blade, click the dots next to the `CallStream` input and then select **Sample data from input**.
+2. On the **Query** page for your Stream Analytics job, click the dots next to the `CallStream` input and then select **Sample data from input**.
 
 3. Specify that you want three minutes' worth of data and click **OK**. Wait until you're notified that the data has been sampled.
 
-4. Click **Test** and make sure you're getting results.
-
+4. Click **Test** and review the results.
 
 ## Run the job
 
-1. Make sure that the TelcoStreaming app is running.
-
-2. Close the **Query** blade.
+1. Make sure the TelcoStreaming app is running.
 
-3. In the job blade, click **Start**.
+2. Navigate to the **Overview** page for your Stream Analytics job and select **Start**.
 
     ![Start the Stream Analytics job](./media/stream-analytics-power-bi-dashboard/stream-analytics-sa-job-start-output.png)
 

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/power-bi-authorization-credentials.png

@@ -4,10 +4,10 @@ description: This quickstart shows you how to get started by creating a Stream A
 services: stream-analytics
 author: mamccrea
 ms.author: mamccrea
-ms.date: 12/20/2018
+ms.date: 06/11/2019
 ms.topic: quickstart
 ms.service: stream-analytics
-ms.custom: mvc
+
 #Customer intent: "As an IT admin/developer I want to create a Stream Analytics job, configure input and output & analyze data by using Visual Studio."
 ---
 

articles/stream-analytics/media/stream-analytics-with-azure-functions/image1.png

@@ -125,11 +125,12 @@ Before you start the TelcoGenerator app, you must configure it so that it will s
 
 ### Start the app
 1.	Open a command window and change to the folder where the TelcoGenerator app is unzipped.
+
 2.	Enter the following command:
 
-        ```cmd
-        telcodatagen.exe 1000 0.2 2
-        ```
+   ```cmd
+   telcodatagen.exe 1000 0.2 2
+   ```
 
     The parameters are: 
 

articles/stream-analytics/media/stream-analytics-with-azure-functions/image2.png

@@ -7,14 +7,13 @@ ms.author: jeanb
 ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 02/05/2019
-ms.custom: seodec18 
+ms.date: 06/11/2019
 ---
 # Set up alerts for Azure Stream Analytics jobs
 
 It's important to monitor your Azure Stream Analytics job to ensure the job is running continuously without any problems. This article describes how to set up alerts for common scenarios that should be monitored. 
 
-Rules can be set up on metrics through the portal and can be configured [programmatically](https://code.msdn.microsoft.com/windowsazure/Receive-Email-Notifications-199e2c9a) over Operation Logs data.
+You can define rules on metrics from Operation Logs data through the portal, as well as [programmatically](https://code.msdn.microsoft.com/windowsazure/Receive-Email-Notifications-199e2c9a).
 
 ## Set up alerts in the Azure portal
 
@@ -24,15 +23,15 @@ The following example demonstrates how to set up alerts for when your job enters
 
 2. On the **Job** page, navigate to the **Monitoring** section.  
 
-3. Select **Metrics**, and then click **New alert rule**.
+3. Select **Metrics**, and then **New alert rule**.
 
    ![Azure portal Stream Analytics alerts setup](./media/stream-analytics-set-up-alerts/stream-analytics-set-up-alerts.png)  
 
 4. Your Stream Analytics job name should automatically appear under **RESOURCE**. Click **Add condition**, and select **All Administrative operations** under **Configure signal logic**.
 
    ![Select signal name for Stream Analytics alert](./media/stream-analytics-set-up-alerts/stream-analytics-condition-signal.png)  
 
-5. Under **Configure signal logic**, change **Event Level** to **All** and change **Status** to **Failed**. Leave **Event initiated by** blank and click **Done**.
+5. Under **Configure signal logic**, change **Event Level** to **All** and change **Status** to **Failed**. Leave **Event initiated by** blank and select **Done**.
 
    ![Configure signal logic for Stream Analytics alert](./media/stream-analytics-set-up-alerts/stream-analytics-configure-signal-logic.png) 
 

articles/stream-analytics/stream-analytics-add-inputs.md

@@ -4,13 +4,13 @@ description: This article describes how to use reference data to lookup or corre
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 01/29/2019
+ms.date: 06/11/2019
 ---
 # Using reference data for lookups in Stream Analytics
+
 Reference data (also known as a lookup table) is a finite data set that is static or slowly changing in nature, used to perform a lookup or to correlate with your data stream. For example, in an IoT scenario, you could store metadata about sensors (which don’t change often) in reference data and join it with real time IoT data streams. Azure Stream Analytics loads reference data in memory to achieve low latency stream processing. To make use of reference data in your Azure Stream Analytics job, you will generally use a [Reference Data Join](https://msdn.microsoft.com/library/azure/dn949258.aspx) in your query. 
 
 Stream Analytics supports Azure Blob storage and Azure SQL Database as the storage layer for Reference Data. You can also transform and/or copy reference data to Blob storage from Azure Data Factory to use [any number of cloud-based and on-premises data stores](../data-factory/copy-activity-overview.md).
@@ -37,7 +37,7 @@ To configure your reference data, you first need to create an input that is of t
 
 ### Static reference data
 
-If your reference data is not expected to change, then support for static reference data is enabled by specifying a static path in the input configuration. Azure Stream Analytics picks up the blob from the specified path. {date} and {time} substitution tokens aren't required. Reference data is immutable in Stream Analytics. Therefore, overwriting a static reference data blob is not recommended.
+If your reference data is not expected to change, then support for static reference data is enabled by specifying a static path in the input configuration. Azure Stream Analytics picks up the blob from the specified path. {date} and {time} substitution tokens aren't required. Because reference data is immutable in Stream Analytics, overwriting a static reference data blob is not recommended.
 
 ### Generate reference data on a schedule
 
@@ -48,7 +48,7 @@ Azure Stream Analytics automatically scans for refreshed reference data blobs at
 > [!NOTE]
 > Currently Stream Analytics jobs look for the blob refresh only when the machine time advances to the time encoded in the blob name. For example, the job will look for `sample/2015-04-16/17-30/products.csv` as soon as possible but no earlier than 5:30 PM on April 16th, 2015 UTC time zone. It will *never* look for a blob with an encoded time earlier than the last one that is discovered.
 > 
-> E.g. once the job finds the blob `sample/2015-04-16/17-30/products.csv` it will ignore any files with an encoded date earlier than 5:30 PM April 16th, 2015 so if a late arriving `sample/2015-04-16/17-25/products.csv` blob gets created in the same container the job will not use it.
+> For example, once the job finds the blob `sample/2015-04-16/17-30/products.csv` it will ignore any files with an encoded date earlier than 5:30 PM April 16th, 2015 so if a late arriving `sample/2015-04-16/17-25/products.csv` blob gets created in the same container the job will not use it.
 > 
 > Likewise if `sample/2015-04-16/17-30/products.csv` is only produced at 10:03 PM April 16th, 2015 but no blob with an earlier date is present in the container, the job will use this file starting at 10:03 PM April 16th, 2015 and use the previous reference data until then.
 > 

articles/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices.md

@@ -4,16 +4,16 @@ description: This article describes four windowing functions (tumbling, hopping,
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 05/07/2018
+ms.date: 06/11/2019
 ---
 # Introduction to Stream Analytics windowing functions
+
 In time-streaming scenarios, performing operations on the data contained in temporal windows is a common pattern. Stream Analytics has native support for windowing functions, enabling developers to author complex stream processing jobs with minimal effort.
 
-There are four kinds of temporal windows to choose from: [**Tumbling**](https://msdn.microsoft.com/azure/stream-analytics/reference/tumbling-window-azure-stream-analytics), [**Hopping**](https://msdn.microsoft.com/azure/stream-analytics/reference/hopping-window-azure-stream-analytics), [**Sliding**](https://msdn.microsoft.com/azure/stream-analytics/reference/sliding-window-azure-stream-analytics), and [**Session**](https://msdn.microsoft.com/azure/stream-analytics/reference/session-window-azure-stream-analytics) windows.  You use the window functions in the [**GROUP BY**](https://msdn.microsoft.com/azure/stream-analytics/reference/group-by-azure-stream-analytics) clause of the query syntax in your Stream Analytics jobs.
+There are four kinds of temporal windows to choose from: [**Tumbling**](https://msdn.microsoft.com/azure/stream-analytics/reference/tumbling-window-azure-stream-analytics), [**Hopping**](https://msdn.microsoft.com/azure/stream-analytics/reference/hopping-window-azure-stream-analytics), [**Sliding**](https://msdn.microsoft.com/azure/stream-analytics/reference/sliding-window-azure-stream-analytics), and [**Session**](https://msdn.microsoft.com/azure/stream-analytics/reference/session-window-azure-stream-analytics) windows.  You use the window functions in the [**GROUP BY**](https://msdn.microsoft.com/azure/stream-analytics/reference/group-by-azure-stream-analytics) clause of the query syntax in your Stream Analytics jobs. You can also aggregate events over multiple windows using the [**Windows()** function](https://docs.microsoft.com/stream-analytics-query/windows-azure-stream-analytics).
 
 All the [windowing](https://msdn.microsoft.com/azure/stream-analytics/reference/windowing-azure-stream-analytics) operations output results at the **end** of the window. The output of the window will be single event based on the aggregate function used. The output event will have the time stamp of the end of the window and all window functions are defined with a fixed length. 
 

articles/stream-analytics/stream-analytics-power-bi-dashboard.md

@@ -7,14 +7,14 @@ ms.service: stream-analytics
 ms.topic: tutorial
 ms.custom: mvc
 ms.workload: data-services
-ms.date: 04/09/2018
+ms.date: 06/05/2019
 ms.author: mamccrea
 ms.reviewer: jasonh
 
 #Customer intent: "As an IT admin/developer I want to run Azure Functions with Stream Analytics jobs."
 ---
 
-# Run Azure Functions from Azure Stream Analytics jobs 
+# Tutorial: Run Azure Functions from Azure Stream Analytics jobs 
 
 You can run Azure Functions from Azure Stream Analytics by configuring Functions as one of the output sinks to the Stream Analytics job. Functions are an event-driven, compute-on-demand experience that lets you implement code that is triggered by events occurring in Azure or third-party services. This ability of Functions to respond to triggers makes it a natural output to Stream Analytics jobs.
 
@@ -23,9 +23,10 @@ Stream Analytics invokes Functions through HTTP triggers. The Functions output a
 In this tutorial, you learn how to:
 
 > [!div class="checklist"]
-> * Create a Stream Analytics job
-> * Create an Azure function
-> * Configure Azure function as output to your job
+> * Create and run a Stream Analytics job
+> * Create an Azure Cache for Redis instance
+> * Create an Azure Function
+> * Check Azure Cache for Redis for results
 
 If you don’t have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 
@@ -35,16 +36,9 @@ This section demonstrates how to configure a Stream Analytics job to run a funct
 
 ![Diagram showing relationships among the Azure services](./media/stream-analytics-with-azure-functions/image1.png)
 
-The following steps are required to achieve this task:
-* [Create a Stream Analytics job with Event Hubs as input](#create-a-stream-analytics-job-with-event-hubs-as-input)  
-* Create an Azure Cache for Redis instance  
-* Create a function in Azure Functions that can write data to the Azure Cache for Redis    
-* [Update the Stream Analytics job with the function as output](#update-the-stream-analytics-job-with-the-function-as-output)  
-* Check Azure Cache for Redis for results  
-
 ## Create a Stream Analytics job with Event Hubs as input
 
-Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detection.md) tutorial to create an event hub, start the event generator application, and create a Stream Analytics job. (Skip the steps to create the query and the output. Instead, see the following sections to set up Functions output.)
+Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detection.md) tutorial to create an event hub, start the event generator application, and create a Stream Analytics job. Skip the steps to create the query and the output. Instead, see the following sections to set up an Azure Functions output.
 
 ## Create an Azure Cache for Redis instance
 
@@ -58,7 +52,7 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
 
 1. See the [Create a function app](../azure-functions/functions-create-first-azure-function.md#create-a-function-app) section of the Functions documentation. This walks you through how to create a function app and an [HTTP-triggered function in Azure Functions](../azure-functions/functions-create-first-azure-function.md#create-function), by using the CSharp language.  
 
-2. Browse to the **run.csx** function. Update it with the following code. (Make sure to replace "\<your Azure Cache for Redis connection string goes here\>" with the Azure Cache for Redis primary connection string that you retrieved in the previous section.)  
+2. Browse to the **run.csx** function. Update it with the following code. Replace **"\<your Azure Cache for Redis connection string goes here\>"** with the Azure Cache for Redis primary connection string that you retrieved in the previous section. 
 
     ```csharp
     using System;
@@ -109,7 +103,7 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
 
    ```
 
-   When Stream Analytics receives the "HTTP Request Entity Too Large" exception from the function, it reduces the size of the batches it sends to Functions. In your function, use the following code to check that Stream Analytics doesn’t send oversized batches. Make sure that the maximum batch count and size values used in the function are consistent with the values entered in the Stream Analytics portal.
+   When Stream Analytics receives the "HTTP Request Entity Too Large" exception from the function, it reduces the size of the batches it sends to Functions. The following code ensures that Stream Analytics doesn't send oversized batches. Make sure that the maximum batch count and size values used in the function are consistent with the values entered in the Stream Analytics portal.
 
     ```csharp
     if (dataArray.ToString().Length > 262144)
@@ -118,7 +112,7 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
         }
    ```
 
-3. In a text editor of your choice, create a JSON file named **project.json**. Use the following code, and save it on your local computer. This file contains the NuGet package dependencies required by the C# function.  
+3. In a text editor of your choice, create a JSON file named **project.json**. Paste the following code, and save it on your local computer. This file contains the NuGet package dependencies required by the C# function.  
 
     ```json
     {
@@ -142,8 +136,6 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
 
    ![Screenshot of App Service Editor](./media/stream-analytics-with-azure-functions/image4.png)
 
- 
-
 ## Update the Stream Analytics job with the function as output
 
 1. Open your Stream Analytics job on the Azure portal.  
@@ -160,9 +152,9 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
    |Max Batch Count|Specifies the maximum number of events in each batch that is sent to the function. The default value is 100. This property is optional.|
    |Key|Allows you to use a function from another subscription. Provide the key value to access your function. This property is optional.|
 
-3. Provide a name for the output alias. In this tutorial, we name it **saop1** (you can use any name of your choice). Fill in other details.  
+3. Provide a name for the output alias. In this tutorial, it is named **saop1**, but you can use any name of your choice. Fill in other details.
 
-4. Open your Stream Analytics job, and update the query to the following. (Make sure to replace the "saop1" text if you have named the output sink differently.)  
+4. Open your Stream Analytics job, and update the query to the following. If you did not name your output sink **saop1**, remember to change it in the query.  
 
    ```sql
     SELECT
@@ -175,9 +167,11 @@ Follow the [Real-time fraud detection](stream-analytics-real-time-fraud-detectio
         WHERE CS1.SwitchNum != CS2.SwitchNum
    ```
 
-5. Start the telcodatagen.exe application by running the following command in command line (use the format `telcodatagen.exe [#NumCDRsPerHour] [SIM Card Fraud Probability] [#DurationHours]`):  
+5. Start the telcodatagen.exe application by running the following command in command line. The command uses the format `telcodatagen.exe [#NumCDRsPerHour] [SIM Card Fraud Probability] [#DurationHours]`.  
 
-   **telcodatagen.exe 1000 .2 2**
+   ```cmd
+   telcodatagen.exe 1000 0.2 2
+   ```
 
 6.	Start the Stream Analytics job.