seo fixes

Author: mamccrea

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/add-fraud.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/add-fraudulent-calls-tile.png

@@ -4,11 +4,11 @@ description: This article describes how to use a real-time Power BI dashboard to
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
-ms.reviewer: jasonh
+ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 06/27/2017
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 # Tutorial: 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.
@@ -38,13 +38,13 @@ In the real-time fraud detection tutorial, the output is sent to Azure Blob stor
 
 4. Under **Sink**, select **Power BI**.
 
-   ![Create an output for Power BI](./media/stream-analytics-power-bi-dashboard/create-pbi-ouptut.png)
+   ![Create an output for Power BI](./media/stream-analytics-power-bi-dashboard/create-power-bi-ouptut.png)
 
 5. Click **Authorize**.
 
     A window opens where you can provide your Azure credentials for a work or school account. 
 
-    ![Enter credentials for access to Power BI](./media/stream-analytics-power-bi-dashboard/authorize-area.png)
+    ![Enter credentials for access to Power BI](./media/stream-analytics-power-bi-dashboard/power-bi-authorization-credentials.png)
 
 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.
 
@@ -54,7 +54,7 @@ In the real-time fraud detection tutorial, the output is sent to Azure Blob stor
     * **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.
 
-    ![PBI workspace](./media/stream-analytics-power-bi-dashboard/create-pbi-ouptut-with-dataset-table.png)
+    ![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.
@@ -84,6 +84,7 @@ 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 */
 
@@ -101,6 +102,7 @@ For more information about Power BI datasets, see the [Power BI REST API](https:
         /* Where the switch location is different */
         WHERE CS1.SwitchNum != CS2.SwitchNum
         GROUP BY TumblingWindow(Duration(second, 1))
+        ```
 
 4. Click **Save**.
 
@@ -114,7 +116,7 @@ This section is optional, but recommended.
     * Go to the folder where the telcogenerator.exe and modified telcodatagen.exe.config files are.
     * Run the following command:
 
-            telcodatagen.exe 1000 .2 2
+       `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**.
 
@@ -140,7 +142,7 @@ Your Streaming Analytics job starts looking for fraudulent calls in the incoming
 
 1. Go to [Powerbi.com](https://powerbi.com) and sign in with your work or school account. If the Stream Analytics job query outputs results, you see that your dataset is already created:
 
-    ![Streaming dataset in Power BI](./media/stream-analytics-power-bi-dashboard/streaming-dataset.png)
+    ![Streaming dataset location in Power BI](./media/stream-analytics-power-bi-dashboard/stream-analytics-streaming-dataset.png)
 
 2. In your workspace, click **+ Create**.
 
@@ -152,15 +154,15 @@ Your Streaming Analytics job starts looking for fraudulent calls in the incoming
 
 4. At the top of the window, click **Add tile**, select **CUSTOM STREAMING DATA**, and then click **Next**.
 
-    ![Custom streaming dataset](./media/stream-analytics-power-bi-dashboard/custom-streaming-data.png)
+    ![Custom streaming dataset tile in Power BI](./media/stream-analytics-power-bi-dashboard/custom-streaming-data.png)
 
 5. Under **YOUR DATSETS**, select your dataset and then click **Next**.
 
-    ![Your streaming dataset](./media/stream-analytics-power-bi-dashboard/your-streaming-dataset.png)
+    ![Your streaming dataset in Power BI](./media/stream-analytics-power-bi-dashboard/your-streaming-dataset.png)
 
 6. Under **Visualization Type**, select **Card**, and then in the **Fields** list, select **fraudulentcalls**.
 
-    ![Visualization details for new tile](./media/stream-analytics-power-bi-dashboard/add-fraud.png)
+    ![Visualization details for new tile](./media/stream-analytics-power-bi-dashboard/add-fraudulent-calls-tile.png)
 
 7. Click **Next**.
 
@@ -172,7 +174,7 @@ Your Streaming Analytics job starts looking for fraudulent calls in the incoming
 
     Now you have a fraud counter!
 
-    ![Fraud counter](./media/stream-analytics-power-bi-dashboard/fraud-counter.png)
+    ![Fraud counter in Power BI dashboard](./media/stream-analytics-power-bi-dashboard/power-bi-fraud-counter-tile.png)
 
 8. Follow the steps again to add a tile (starting with step 4). This time, do the following:
 
@@ -181,7 +183,7 @@ Your Streaming Analytics job starts looking for fraudulent calls in the incoming
     * Add a value and select **fraudulentcalls**.
     * For **Time window to display**, select the last 10 minutes.
 
-    ![Create tile for line chart](./media/stream-analytics-power-bi-dashboard/pbi-create-tile-line-chart.png)
+    ![Create tile for line chart in Power BI](./media/stream-analytics-power-bi-dashboard/pbi-create-tile-line-chart.png)
 
 9. Click **Next**, add a title and subtitle, and click **Apply**.
 
@@ -204,7 +206,7 @@ Currently, Power BI can be called roughly once per second. Streaming visuals sup
 
 You can use the following equation to compute the value to give your window in seconds:
 
-![Equation1](./media/stream-analytics-power-bi-dashboard/equation1.png)  
+![Equation to compute value to give window in seconds](./media/stream-analytics-power-bi-dashboard/compute-window-seconds-equation.png)  
 
 For example:
 
@@ -214,10 +216,11 @@ For example:
 
 As a result, the equation becomes:
 
-![Equation2](./media/stream-analytics-power-bi-dashboard/equation2.png)  
+![Equation based on example criteria](./media/stream-analytics-power-bi-dashboard/power-bi-example-equation.png)  
 
 Given this configuration, you can change the original query to the following:
 
+```SQL
     SELECT
         MAX(hmdt) AS hmdt,
         MAX(temp) AS temp,
@@ -229,7 +232,7 @@ Given this configuration, you can change the original query to the following:
     GROUP BY
         TUMBLINGWINDOW(ss,4),
         dspl
-
+```
 
 ### Renew authorization
 If the password has changed since your job was created or last authenticated, you need to reauthenticate your Power BI account. If Azure Multi-Factor Authentication is configured on your Azure Active Directory (Azure AD) tenant, you also need to renew Power BI authorization every two weeks. If you don't renew, you could see symptoms such as a lack of job output or an `Authenticate user error` in the operation logs.

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/equation1.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/compute-window-seconds-equation.png

@@ -7,7 +7,7 @@ ms.author: mamccrea
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 10/05/2018
+ms.date: 12/07/2018
 ---
 
 # Azure Stream Analytics preview features
@@ -18,10 +18,6 @@ This article summarizes all the features currently in preview for Azure Stream A
 
 The following features are in public preview. You can take advantage of these features today, but don't use them in your production environment.
 
-### Azure Stream Analytics on IoT Edge
-
-Azure Stream Analytics on IoT Edge allows developers to deploy near-real-time analytics on IoT Edge devices. For more information, see the [Azure Stream Analytics on IoT Edge](stream-analytics-edge.md) documentation.
-
 ### Integration with Azure Machine Learning
 
 You can scale Stream Analytics jobs with Machine Learning (ML) functions. To learn more about how you can use ML functions in your Stream Analytics job, visit [Scale your Stream Analytics job with Azure Machine Learning functions](stream-analytics-scale-with-machine-learning-functions.md). Check out a real-world scenario with [Performing sentiment analysis by using Azure Stream Analytics and Azure Machine Learning](stream-analytics-machine-learning-integration-tutorial.md).

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

@@ -4,7 +4,6 @@ description: This article describes the reference architecture to achieve real-t
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/authorize-area.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/power-bi-authorization-credentials.png

@@ -4,11 +4,11 @@ description: Learn how to create a real-time fraud detection solution with Strea
 services: stream-analytics 
 author: mamccrea
 ms.author: mamccrea
-manager: kfile 
 ms.reviewer: jasonh
 ms.service: stream-analytics 
 ms.topic: conceptual 
-ms.date: 03/28/2017 
+ms.date: 12/07/2018
+ms.custom: seodec18 
 --- 
 # Get started using Azure Stream Analytics: Real-time fraud detection
 
@@ -38,7 +38,7 @@ Before you start, make sure you have the following:
     >[!NOTE]
     >Windows might block the downloaded .zip file. If you can't unzip it, right-click the file and select **Properties**. If you see the "This file came from another computer and might be blocked to help protect this computer" message, select the **Unblock** option and then click **Apply**.
 
-If you want to examine the results of the Streaming Analytics job, you also need a tool for viewing the contents of an Azure Blob Storage container. If you use Visual Studio, you can use [Azure Tools for Visual Studio](https://docs.microsoft.com/azure/vs-azure-tools-storage-resources-server-explorer-browse-manage) or [Visual Studio Cloud Explorer](https://docs.microsoft.com/azure/vs-azure-tools-resources-managing-with-cloud-explorer). Alternatively, you can install standalone tools like [Azure Storage Explorer](http://storageexplorer.com/) or [Azure Explorer](http://www.cerebrata.com/products/azure-explorer/introduction). 
+If you want to examine the results of the Streaming Analytics job, you also need a tool for viewing the contents of an Azure Blob Storage container. If you use Visual Studio, you can use [Azure Tools for Visual Studio](https://docs.microsoft.com/azure/vs-azure-tools-storage-resources-server-explorer-browse-manage) or [Visual Studio Cloud Explorer](https://docs.microsoft.com/azure/vs-azure-tools-resources-managing-with-cloud-explorer). Alternatively, you can install standalone tools like [Azure Storage Explorer](https://storageexplorer.com/) or [Cerulean](https://www.cerebrata.com/products/cerulean/features/azure-storage). 
 
 ## Create an Azure Event Hubs to ingest events
 
@@ -56,7 +56,7 @@ In this procedure, you first create an event hub namespace, and then you add an
 
 3. Select a subscription and create or choose a resource group, then click **Create**.
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-eventhub-namespace-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-eventhub-namespace-new-portal.png" alt="Create event hub namespace in Azure portal" width="300px"/>
 
 4. When the namespace has finished deploying, find the event hub namespace in your list of Azure resources. 
 
@@ -66,7 +66,7 @@ In this procedure, you first create an event hub namespace, and then you add an
 
 6. Name the new event hub `asa-eh-frauddetection-demo`. You can use a different name. If you do, make a note of it, because you need the name later. You don't need to set any other options for the event hub right now.
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-eventhub-new-portal.png" alt="drawing" width="400px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-eventhub-new-portal.png" alt="Name event hub in Azure portal" width="400px"/>
 
 
 7. Click **Create**.
@@ -84,15 +84,15 @@ Before a process can send data to an event hub, the event hub must have a policy
 
 3.	Add a policy named `sa-policy-manage-demo` and for **Claim**, select **Manage**.
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-shared-access-policy-manage-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-shared-access-policy-manage-new-portal.png" alt="Create shared access policy for Stream Analytics" width="300px"/>
 
 4.	Click **Create**.
 
 5.	After the policy has been deployed, click it in the list of shared access policies.
 
 6.	Find the box labeled **CONNECTION STRING-PRIMARY KEY** and click the copy button next to the connection string. 
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-shared-access-policy-copy-connection-string-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-shared-access-policy-copy-connection-string-new-portal.png" alt="Stream Analytics shared access policy" width="300px"/>
 
 7.	Paste the connection string into a text editor. You need this connection string for the next section, after you make some small edits to it.
 
@@ -119,15 +119,17 @@ Before you start the TelcoGenerator app, you must configure it so that it will s
 
     The `<appSettings>` section will look like the following example. (For clarity, the lines are wrapped and some characters have been removed from the authorization token.)
 
-   ![TelcoGenerator app configuration file showing the event hub name and connection string](./media/stream-analytics-real-time-fraud-detection/stream-analytics-telcogenerator-config-file-app-settings.png)
+   ![TelcoGenerator config file shows event hub name and connection string](./media/stream-analytics-real-time-fraud-detection/stream-analytics-telcogenerator-config-file-app-settings.png)
 
 4.	Save the file. 
 
 ### 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
+        ```
 
     The parameters are: 
 
@@ -161,7 +163,7 @@ Now that you have a stream of call events, you can set up a Stream Analytics job
 
     It's a good idea to place the job and the event hub in the same region for best performance and so that you don't pay to transfer data between regions.
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-sa-job-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-sa-job-new-portal.png" alt="Create Stream Analytics job in portal" width="300px"/>
 
 3. Click **Create**.
 
@@ -184,7 +186,7 @@ Now that you have a stream of call events, you can set up a Stream Analytics job
    |Event Hub name  | asa-eh-frauddetection-demo | Select the name of your Event Hub.   |
    |Event Hub policy name  | asa-policy-manage-demo | Select the access policy that you created earlier.   |
     </br>
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-sa-input-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-sa-input-new-portal.png" alt="Create Stream Analytics input in portal" width="300px"/>
 
 
 4. Click **Create**.
@@ -213,7 +215,7 @@ The TelcoGenerator app is sending call records to the event hub, and your Stream
 
 5. Set **Minutes** to 3 and then click **OK**. 
 
-   ![Options for sampling the input stream, with "3 minutes" selected.](./media/stream-analytics-real-time-fraud-detection/stream-analytics-input-create-sample-data.png)
+   ![Options for sampling input stream with 3 minutes selected](./media/stream-analytics-real-time-fraud-detection/stream-analytics-input-create-sample-data.png)
 
     Azure samples 3 minutes' worth of data from the input stream and notifies you when the sample data is ready. (This takes a short while.) 
 
@@ -226,11 +228,13 @@ As an alternative, you can get a .json file that has sample data in it [from Git
 If you want to archive every event, you can use a pass-through query to read all the fields in the payload of the event.
 
 1. In the query window, enter this query:
-
-        SELECT 
-            *
-        FROM 
-            CallStream
+        
+   ```SQL
+   SELECT 
+       *
+   FROM 
+       CallStream
+   ```
 
     >[!NOTE]
     >As with SQL, keywords are not case-sensitive, and whitespace is not significant.
@@ -251,13 +255,15 @@ In many cases, your analysis doesn't need all the columns from the input stream.
 
 1. Change the query in the code editor to the following:
 
-        SELECT CallRecTime, SwitchNum, CallingIMSI, CallingNum, CalledNum 
-        FROM 
-            CallStream
+   ```SQL
+   SELECT CallRecTime, SwitchNum, CallingIMSI, CallingNum, CalledNum 
+   FROM 
+       CallStream
+   ```
 
 2. Click **Test** again. 
 
-   ![Stream Analytics job output for projection, showing 25 records generated](./media/stream-analytics-real-time-fraud-detection/stream-analytics-sa-job-sample-output-projection.png)
+   ![Stream Analytics job output for projection shows 25 records](./media/stream-analytics-real-time-fraud-detection/stream-analytics-sa-job-sample-output-projection.png)
 
 ### Count incoming calls by region: Tumbling window with aggregation
 
@@ -267,11 +273,13 @@ For this transformation, you want a sequence of temporal windows that don't over
 
 1. Change the query in the code editor to the following:
 
+        ```SQL
         SELECT 
             System.Timestamp as WindowEnd, SwitchNum, COUNT(*) as CallCount 
         FROM
             CallStream TIMESTAMP BY CallRecTime 
         GROUP BY TUMBLINGWINDOW(s, 5), SwitchNum
+        ```
 
     This query uses the `Timestamp By` keyword in the `FROM` clause to specify which timestamp field in the input stream to use to define the Tumbling window. In this case, the window divides the data into segments by the `CallRecTime` field in each record. (If no field is specified, the windowing operation uses the time that each event arrives at the event hub. See "Arrival Time Vs Application Time" in [Stream Analytics Query Language Reference](https://msdn.microsoft.com/library/azure/dn834998.aspx). 
 
@@ -281,7 +289,7 @@ For this transformation, you want a sequence of temporal windows that don't over
 
 2. Click **Test** again. In the results, notice that the timestamps under **WindowEnd** are in 5-second increments.
 
-   ![Stream Analytics job output for aggregation, showing 13 records generated](./media/stream-analytics-real-time-fraud-detection/stream-analytics-sa-job-sample-output-aggregation.png)
+   ![Stream Analytics job output for aggregation showing 13 records](./media/stream-analytics-real-time-fraud-detection/stream-analytics-sa-job-sample-output-aggregation.png)
 
 ### Detect SIM fraud using a self-join
 
@@ -293,6 +301,7 @@ When you use a join with streaming data, the join must provide some limits on ho
 
 1. Change the query in the code editor to the following: 
 
+        ```SQL
         SELECT  System.Timestamp as Time, 
             CS1.CallingIMSI, 
             CS1.CallingNum as CallingNum1, 
@@ -304,6 +313,7 @@ When you use a join with streaming data, the join must provide some limits on ho
             ON CS1.CallingIMSI = CS2.CallingIMSI 
             AND DATEDIFF(ss, CS1, CS2) BETWEEN 1 AND 5 
         WHERE CS1.SwitchNum != CS2.SwitchNum
+        ```
 
     This query is like any SQL join except for the `DATEDIFF` function in the join. This version of `DATEDIFF` is specific to Streaming Analytics, and it must appear in the `ON...BETWEEN` clause. The parameters are a time unit (seconds in this example) and the aliases of the two sources for the join. This is different from the standard SQL `DATEDIFF` function.
 
@@ -315,7 +325,7 @@ When you use a join with streaming data, the join must provide some limits on ho
 
 3. Click **Save** to save the self-join query as part of the Streaming Analytics job. (It doesn't save the sample data.)
 
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-query-editor-save-button-new-portal.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-query-editor-save-button-new-portal.png" alt="Save Stream Analytics query in portal" width="300px"/>
 
 ## Create an output sink to store transformed data
 
@@ -329,7 +339,7 @@ If you have an existing blob storage account, you can use that. For this tutoria
 
 1. From the upper left-hand corner of the Azure portal, select **Create a resource** > **Storage** > **Storage account**. Fill out the Storage account job page with **Name** set to "asaehstorage", **Location** set to "East US", **Resource group** set to "asa-eh-ns-rg" (host the storage account in the same resource group as the Streaming job for increased performance). The remaining settings can be left to their default values.  
 
-   ![Create storage account](./media/stream-analytics-real-time-fraud-detection/stream-analytics-storage-account-create.png)
+   ![Create storage account in Azure portal](./media/stream-analytics-real-time-fraud-detection/stream-analytics-storage-account-create.png)
 
 2. In the Azure portal, return to the Streaming Analytics job pane. (If you closed the pane, search for `asa_frauddetection_job_demo` in the **All resources** pane.)
 
@@ -344,7 +354,7 @@ If you have an existing blob storage account, you can use that. For this tutoria
    |Storage account  |  asaehstorage |  Enter the name of the storage account you created. |
    |Container  | asa-fraudulentcalls-demo | Choose Create new and enter a container name. |
     <br/>
-    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-output-blob-storage-new-console.png" alt="drawing" width="300px"/>
+    <img src="./media/stream-analytics-real-time-fraud-detection/stream-analytics-create-output-blob-storage-new-console.png" alt="Create blob output for Stream Analytics job" width="300px"/>
 
 5. Click **Save**. 
 
@@ -365,7 +375,7 @@ The job is now configured. You've specified an input (the event hub), a transfor
 
 You now have a complete Streaming Analytics job. The job is examining a stream of phone call metadata, looking for fraudulent phone calls in real time, and writing information about those fraudulent calls to storage. 
 
-To complete this tutorial, you might want to look at the data being captured by the Streaming Analytics job. The data is being written to Azure Blog Storage in chunks (files). You can use any tool that reads Azure Blob Storage. As noted in the Prerequisites section, you can use Azure extensions in Visual Studio, or you can use a tool like [Azure Storage Explorer](http://storageexplorer.com/) or [Azure Explorer](http://www.cerebrata.com/products/azure-explorer/introduction). 
+To complete this tutorial, you might want to look at the data being captured by the Streaming Analytics job. The data is being written to Azure Blog Storage in chunks (files). You can use any tool that reads Azure Blob Storage. As noted in the Prerequisites section, you can use Azure extensions in Visual Studio, or you can use a tool like [Azure Storage Explorer](https://storageexplorer.com/) or [Cerulean](https://www.cerebrata.com/products/cerulean/features/azure-storage). 
 
 When you examine the contents of a file in blob storage, you see something like the following:
 

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/equation2.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/power-bi-example-equation.png

@@ -43,15 +43,16 @@ If your query is not embarrassingly parallel, you can follow the following steps
 
 Query:
 
-    WITH Step1 AS (
-    SELECT COUNT(*) AS Count, TollBoothId, PartitionId
-    FROM Input1 Partition By PartitionId
-    GROUP BY TumblingWindow(minute, 3), TollBoothId, PartitionId
-    )
-    SELECT SUM(Count) AS Count, TollBoothId
-    FROM Step1
-    GROUP BY TumblingWindow(minute, 3), TollBoothId
-
+ ```SQL
+ WITH Step1 AS (
+ SELECT COUNT(*) AS Count, TollBoothId, PartitionId
+ FROM Input1 Partition By PartitionId
+ GROUP BY TumblingWindow(minute, 3), TollBoothId, PartitionId
+ )
+ SELECT SUM(Count) AS Count, TollBoothId
+ FROM Step1
+ GROUP BY TumblingWindow(minute, 3), TollBoothId
+ ```
 In the query above, you are counting cars per toll booth per partition, and then adding the count from all partitions together.
 
 Once partitioned, for each partition of the step, allocate up to 6 SU, each partition having 6 SU is the maximum, so each partition can be placed on its own processing node.

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/fraud-counter.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/power-bi-fraud-counter-tile.png

@@ -4,7 +4,6 @@ description: This article describes how to scale Stream Analytics jobs that use
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
@@ -40,14 +39,15 @@ The following example includes a Stream Analytics job with the sentiment analysi
 
 The query is a simple fully partitioned query followed by the **sentiment** function, as shown following:
 
+```SQL
     WITH subquery AS (
         SELECT text, sentiment(text) as result from input
     )
 
     Select text, result.[Score]
     Into output
     From subquery
-
+```
 Consider the following scenario; with a throughput of 10,000 tweets per second a Stream Analytics job must be created to perform sentiment analysis of the tweets (events). Using 1 SU, could this Stream Analytics job be able to handle the traffic? Using the default batch size of 1000 the job should be able to keep up with the input. Further the added Machine Learning function should generate no more than a second of latency, which is the general default latency of the sentiment analysis Machine Learning web service (with a default batch size of 1000). The Stream Analytics job’s **overall** or end-to-end latency would typically be a few seconds. Take a more detailed look into this Stream Analytics job, *especially* the Machine Learning function calls. Having the batch size as 1000, a throughput of 10,000 events take about 10 requests to web service. Even with 1 SU, there are enough concurrent connections to accommodate this input traffic.
 
 If the input event rate increases by 100x, then the Stream Analytics job needs to process 1,000,000 tweets per second. There are two options to accomplish the increased scale:

articles/stream-analytics/media/stream-analytics-power-bi-dashboard/streaming-dataset.png renamed to articles/stream-analytics/media/stream-analytics-power-bi-dashboard/stream-analytics-streaming-dataset.png

@@ -4,11 +4,11 @@ description: This article describes how to use the Azure portal to set up monito
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
-ms.reviewer: jasonh
+ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 06/26/2017
+ms.date: 12/07/2018
+ms.custom: seodec18 
 ---
 # Set up alerts for Azure Stream Analytics jobs
 You can set up alerts to trigger an alert when a metric reaches a condition that you specify. For example, you might set up an alert for a condition like the following:
@@ -24,7 +24,7 @@ Rules can be set up on metrics through the portal, or can be configured [program
 
 3. In the **Metric** blade, click the **Add alert** command.
 
-      ![Azure portal setup](./media/stream-analytics-set-up-alerts/06-stream-analytics-set-up-alerts.png)  
+      ![Azure portal Stream Analytics alerts setup](./media/stream-analytics-set-up-alerts/06-stream-analytics-set-up-alerts.png)  
 
 4. Enter a name and a description.
 

articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/new-edge-project.png renamed to articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/new-stream-analytics-edge-project.png

@@ -3,7 +3,6 @@ title: Common query patterns in Azure Stream Analytics
 description: This article describes a number of common query patterns and designs that are useful in Azure Stream Analytics jobs.
 services: stream-analytics
 author: jseb225
-manager: kfile
 ms.author: jeanb
 ms.reviewer: jasonh
 ms.service: stream-analytics
@@ -43,6 +42,7 @@ For example, the car weight is coming on the input stream as strings and needs t
 
 **Solution**:
 
+```SQL
     SELECT
         Make,
         SUM(CAST(Weight AS BIGINT)) AS Weight
@@ -51,6 +51,7 @@ For example, the car weight is coming on the input stream as strings and needs t
     GROUP BY
         Make,
         TumblingWindow(second, 10)
+```
 
 **Explanation**:
 Use a **CAST** statement in the **Weight** field to specify its data type. See the list of supported data types in [Data types (Azure Stream Analytics)](https://msdn.microsoft.com/library/azure/dn835065.aspx).
@@ -76,12 +77,14 @@ For example, check that the result returns license plates that start with A and
 
 **Solution**:
 
+```SQL
     SELECT
         *
     FROM
         Input TIMESTAMP BY Time
     WHERE
         LicensePlate LIKE 'A%9'
+```
 
 **Explanation**:
 Use the **LIKE** statement to check the **LicensePlate** field value. It should start with an A, then have any string of zero or more characters, and then end with a 9. 
@@ -107,6 +110,7 @@ For example, provide a string description for how many cars of the same make pas
 
 **Solution**:
 
+```SQL
     SELECT
         CASE
             WHEN COUNT(*) = 1 THEN CONCAT('1 ', Make)
@@ -118,6 +122,7 @@ For example, provide a string description for how many cars of the same make pas
     GROUP BY
         Make,
         TumblingWindow(second, 10)
+```
 
 **Explanation**:
 The **CASE** expression compares an expression to a set of simple expressions to determine the result. In this example, vehicle makes with a count of 1 returned a different string description than vehicle makes with a count other than 1. 
@@ -154,6 +159,7 @@ For example, analyze data for a threshold-based alert and archive all events to
 
 **Solution**:
 
+```SQL
     SELECT
         *
     INTO
@@ -174,6 +180,7 @@ For example, analyze data for a threshold-based alert and archive all events to
         TumblingWindow(second, 10)
     HAVING
         [Count] >= 3
+```
 
 **Explanation**:
 The **INTO** clause tells Stream Analytics which of the outputs to write the data to from this statement.
@@ -183,6 +190,7 @@ The second query does some simple aggregation and filtering, and it sends the re
 Note that you can also reuse the results of the common table expressions (CTEs) (such as **WITH** statements) in multiple output statements. This option has the added benefit of opening fewer readers to the input source.
 For example: 
 
+```SQL
     WITH AllRedCars AS (
         SELECT
             *
@@ -193,6 +201,7 @@ For example:
     )
     SELECT * INTO HondaOutput FROM AllRedCars WHERE Make = 'Honda'
     SELECT * INTO ToyotaOutput FROM AllRedCars WHERE Make = 'Toyota'
+```
 
 ## Query example: Count unique values
 **Description**: Count the number of unique field values that appear in the stream within a time window.
@@ -217,14 +226,14 @@ For example, how many unique makes of cars passed through the toll booth in a 2-
 
 **Solution:**
 
-````
+```SQL
 SELECT
      COUNT(DISTINCT Make) AS CountMake,
      System.TIMESTAMP AS TIME
 FROM Input TIMESTAMP BY TIME
 GROUP BY 
      TumblingWindow(second, 2)
-````
+```
 
 
 **Explanation:**
@@ -249,13 +258,15 @@ For example, is the previous car on the toll road the same make as the current c
 
 **Solution**:
 
+```SQL
     SELECT
         Make,
         Time
     FROM
         Input TIMESTAMP BY Time
     WHERE
         LAG(Make, 1) OVER (LIMIT DURATION(minute, 1)) <> Make
+```
 
 **Explanation**:
 Use **LAG** to peek into the input stream one event back and get the **Make** value. Then compare it to the **Make** value on the current event and output the event if they are different.
@@ -284,6 +295,7 @@ Use **LAG** to peek into the input stream one event back and get the **Make** va
 
 **Solution**:
 
+```SQL
     SELECT 
         LicensePlate,
         Make,
@@ -292,6 +304,7 @@ Use **LAG** to peek into the input stream one event back and get the **Make** va
         Input TIMESTAMP BY Time
     WHERE 
         IsFirst(minute, 10) = 1
+```
 
 Now let’s change the problem and find the first car of a particular make in every 10-minute interval.
 
@@ -305,6 +318,7 @@ Now let’s change the problem and find the first car of a particular make in ev
 
 **Solution**:
 
+```SQL
     SELECT 
         LicensePlate,
         Make,
@@ -313,6 +327,7 @@ Now let’s change the problem and find the first car of a particular make in ev
         Input TIMESTAMP BY Time
     WHERE 
         IsFirst(minute, 10) OVER (PARTITION BY Make) = 1
+```
 
 ## Query example: Find the last event in a window
 **Description**: Find the last car in every 10-minute interval.
@@ -338,6 +353,7 @@ Now let’s change the problem and find the first car of a particular make in ev
 
 **Solution**:
 
+```SQL
     WITH LastInWindow AS
     (
         SELECT 
@@ -356,6 +372,7 @@ Now let’s change the problem and find the first car of a particular make in ev
         INNER JOIN LastInWindow
         ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10
         AND Input.Time = LastInWindow.LastEventTime
+```
 
 **Explanation**:
 There are two steps in the query. The first one finds the latest time stamp in 10-minute windows. The second step joins the results of the first query with the original stream to find the events that match the last time stamps in each window. 
@@ -381,6 +398,7 @@ For example, have 2 consecutive cars from the same make entered the toll road wi
 
 **Solution**:
 
+```SQL
     SELECT
         Make,
         Time,
@@ -391,6 +409,7 @@ For example, have 2 consecutive cars from the same make entered the toll road wi
         Input TIMESTAMP BY Time
     WHERE
         LAG(Make, 1) OVER (LIMIT DURATION(second, 90)) = Make
+```
 
 **Explanation**:
 Use **LAG** to peek into the input stream one event back and get the **Make** value. Compare it to the **MAKE** value in the current event, and then output the event if they are the same. You can also use **LAG** to get data about the previous car.
@@ -413,13 +432,13 @@ Use **LAG** to peek into the input stream one event back and get the **Make** va
 
 **Solution**:
 
-````
+```SQL
     SELECT
         [user], feature, DATEDIFF(second, LAST(Time) OVER (PARTITION BY [user], feature LIMIT DURATION(hour, 1) WHEN Event = 'start'), Time) as duration
     FROM input TIMESTAMP BY Time
     WHERE
         Event = 'end'
-````
+```
 
 **Explanation**:
 Use the **LAST** function to retrieve the last **TIME** value when the event type was **Start**. The **LAST** function uses **PARTITION BY [user]** to indicate that the result is computed per unique user. The query has a 1-hour maximum threshold for the time difference between **Start** and **Stop** events, but is configurable as needed **(LIMIT DURATION(hour, 1)**.
@@ -449,7 +468,7 @@ For example, suppose that a bug resulted in all cars having an incorrect weight
 
 **Solution**:
 
-````
+```SQL
     WITH SelectPreviousEvent AS
     (
     SELECT
@@ -466,7 +485,7 @@ For example, suppose that a bug resulted in all cars having an incorrect weight
     WHERE
         [weight] < 20000
         AND previousWeight > 20000
-````
+```
 
 **Explanation**:
 Use **LAG** to view the input stream for 24 hours and look for instances where **StartFault** and **StopFault** are spanned by the weight < 20000.
@@ -503,13 +522,14 @@ For example, generate an event every 5 seconds that reports the most recently se
 
 **Solution**:
 
+```SQL
     SELECT
         System.Timestamp AS windowEnd,
         TopOne() OVER (ORDER BY t DESC) AS lastEvent
     FROM
         input TIMESTAMP BY t
     GROUP BY HOPPINGWINDOW(second, 300, 5)
-
+```
 
 **Explanation**:
 This query generates events every 5 seconds and outputs the last event that was received previously. The [Hopping window](https://msdn.microsoft.com/library/dn835041.aspx "Hopping window--Azure Stream Analytics") duration determines how far back the query looks to find the latest event (300 seconds in this example).
@@ -550,7 +570,7 @@ For example, in an IoT scenario for home ovens, an alert must be generated when
 
 **Solution**:
 
-````
+```SQL
 WITH max_power_during_last_3_mins AS (
     SELECT 
         System.TimeStamp AS windowTime,
@@ -584,7 +604,7 @@ WHERE
     t1.sensorName = 'temp'
     AND t1.value <= 40
     AND t2.maxPower > 10
-````
+```
 
 **Explanation**:
 The first query `max_power_during_last_3_mins`, uses the [Sliding window](https://msdn.microsoft.com/azure/stream-analytics/reference/sliding-window-azure-stream-analytics) to find the max value of the power sensor for every device, during the last 3 minutes. 
@@ -619,15 +639,14 @@ And then, provided the conditions are met, an alert is generated for the device.
 
 **Solution**:
 
-````
+```SQL
 SELECT
       TollId,
       COUNT(*) AS Count
 FROM input
       TIMESTAMP BY Time OVER TollId
 GROUP BY TUMBLINGWINDOW(second, 5), TollId
-
-````
+```
 
 **Explanation**:
 The [TIMESTAMP BY OVER](https://msdn.microsoft.com/azure/stream-analytics/reference/timestamp-by-azure-stream-analytics#over-clause-interacts-with-event-ordering) clause looks at each device timeline separately using substreams. The output events for each TollID are generated as they are computed, meaning that the events are in order with respect to each TollID instead of being reordered as if all devices were on the same clock.

articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/result-folder.png renamed to articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-job-result-folder.png

@@ -1,15 +1,14 @@
 ---
 title: Test an Azure Stream Analytics job with sample data
-description: How to test your queries in Stream Analytics jobs.
-keywords: This article describes how to use the Azure portal to test an Azure Stream Analytics job, sample input, and upload sample data.
+description: This article describes how to use the Azure portal to test an Azure Stream Analytics job, sample input, and upload sample data.
 services: stream-analytics
-author: jasonwhowell
+author: mamccrea
 ms.author: mamccrea
 ms.reviewer: jasonh
-manager: kfile
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 04/27/2018
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 
 # Test a Stream Analytics query with sample data

articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/run-options.png renamed to articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-visual-stuidio-run-options.png

@@ -4,7 +4,6 @@ description: This article describes how to use Visual Studio tools for Azure Str
 services: stream-analytics
 author: su-jie
 ms.author: sujie
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
@@ -74,11 +73,11 @@ localrun -Project [ProjectFullPath]
 
 The *arm* command takes the job template and job template parameter files generated through build as input. Then it combines them into a job definition JSON file that can be used with the Stream Analytics PowerShell API.
 
-```
+```powershell
 arm -JobTemplate <templateFilePath> -JobParameterFile <jobParameterFilePath> [-OutputFile <asaArmFilePath>]
 ```
 Example:
-```
+```powershell
 ./tools/SA.exe arm -JobTemplate "ProjectA.JobTemplate.json" -JobParameterFile "ProjectA.JobTemplate.parameters.json" -OutputFile "JobDefinition.json" 
 ```
 

articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/submit-to-azure.png renamed to articles/stream-analytics/media/stream-analytics-tools-for-visual-studio-edge-jobs/submit-stream-analytics-job-to-azure.png

@@ -4,11 +4,11 @@ description: This article describes how to author, debug, and create your Stream
 services: stream-analytics
 author: su-jie
 ms.author: sujie
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 03/13/2018
+ms.date: 12/07/2018
+ms.custom: seodec18
 --- 
 
 # Develop Stream Analytics Edge jobs using Visual Studio tools
@@ -27,11 +27,11 @@ You need the following prerequisites to complete this tutorial:
 
 From Visual Studio, select **File** > **New** > **Project**. Navigate to the **Templates** list on the left > expand **Azure Stream Analytics** > **Stream Analytics Edge** > **Azure Stream Analytics Edge Application**. Provide a Name, Location, and Solution name for your project and select **OK**.
 
-![New Edge project](./media/stream-analytics-tools-for-visual-studio-edge-jobs/new-edge-project.png)
+![New Edge project in Visual Studio](./media/stream-analytics-tools-for-visual-studio-edge-jobs/new-stream-analytics-edge-project.png)
 
 After the project gets created, navigate to the **Solution Explorer** to view the folder hierarchy.
 
-![Solution explorer view](./media/stream-analytics-tools-for-visual-studio-edge-jobs/edge-project-in-solution-explorer.png)
+![Solution explorer view of Stream Analytics Edge job](./media/stream-analytics-tools-for-visual-studio-edge-jobs/edge-project-in-solution-explorer.png)
 
 
 ## Choose the correct subscription
@@ -45,7 +45,7 @@ After the project gets created, navigate to the **Solution Explorer** to view th
 1. From the **Solution Explorer**, expand the **Inputs** node you should see an input named **EdgeInput.json**. Double-click to view its settings.  
 
 2. Make sure that Source Type is set to **Data Stream** > Source is set to **Edge Hub** > Event Serialization Format set to **Json** > and Encoding is set to **UTF8**. Optionally, you can rename the **Input Alias**, let’s leave it as is for this example. In case you rename the input alias, use the name you specified when defining the query. Select **Save** to save the settings.  
-   ![Input configuration](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-input-configuration.png)
+   ![Stream Analytics job input configuration](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-input-configuration.png)
 
 
 
@@ -54,7 +54,7 @@ After the project gets created, navigate to the **Solution Explorer** to view th
 1. From the **Solution Explorer**, expand the **Outputs** node you should see an output named **EdgeOutput.json**. Double-click to view its settings.  
 
 2. Make sure that Sink is set to select **Edge Hub** > Event Serialization Format set to **Json** > and Encoding is set to **UTF8** > and Format is set to **Array**. Optionally, you can rename the **Output Alias**, let’s leave it as is for this example. In case you rename the output alias, use the name you specified when defining the query. Select **Save** to save the settings. 
-   ![Output configuration](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-output-configuration.png)
+   ![Stream Analytics job output configuration](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-output-configuration.png)
 
 ## Define the transformation query
 
@@ -82,17 +82,17 @@ To test the query locally, you should upload the sample data. You can get sample
 1. To upload sample data, > right click on **EdgeInput.json** file > choose **Add Local Input**  
 
 2. In the pop-up window > **Browse** the sample data from your local path > Select **Save**.
-   ![Local input configuration](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-local-input-configuration.png)
+   ![Local input configuration in Visual Studio](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-local-input-configuration.png)
 
 3. A file named **local_EdgeInput.json** is added automatically to your inputs folder.  
 4. you can either run it locally or submit to Azure. To test the query > Select **Run Locally**.  
-   ![Run options](./media/stream-analytics-tools-for-visual-studio-edge-jobs/run-options.png)
+   ![Stream Analytics job run options in Visual Studio](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-visual-stuidio-run-options.png)
 
 5. The command prompt window shows the status of the job. When the job runs successfully, it creates a folder that looks like "2018-02-23-11-31-42" in your project folder path "Visual Studio 2015\Projects\MyASAEdgejob\MyASAEdgejob\ASALocalRun\2018-02-23-11-31-42". Navigate to the folder path to view the results in the local folder:
 
    You can also sign in to the Azure portal and verify that the job is created. 
 
-   ![Result folder](./media/stream-analytics-tools-for-visual-studio-edge-jobs/result-folder.png)
+   ![Stream Analytics job result folder](./media/stream-analytics-tools-for-visual-studio-edge-jobs/stream-analytics-job-result-folder.png)
 
 ## Submit the job to Azure
 
@@ -102,19 +102,19 @@ To test the query locally, you should upload the sample data. You can get sample
 
 3. A pop-up window opens, where you can choose to update an existing Edge job or create a new one. When you update an existing job, it will replace all the job configuration, in this scenario, you will publish a new job. Select **Create a New Azure Stream Analytics Job** > enter a name for your job something like **MyASAEdgeJob** > choose the required **Subscription**, **Resource Group**, and **Location** > Select **Submit**.
 
-   ![Submit to Azure](./media/stream-analytics-tools-for-visual-studio-edge-jobs/submit-to-azure.png)
+   ![Submit Stream Analytics job to Azure from Visual Studio](./media/stream-analytics-tools-for-visual-studio-edge-jobs/submit-stream-analytics-job-to-azure.png)
 
    Now your Stream Analytics Edge job has been created, you can refer to the [Run jobs on IoT Edge tutorial](stream-analytics-edge.md) to learn how to deploy it to your devices. 
 
 ## Manage the job 
 
 You can view the status of job and the job diagram from the Server Explorer. From the **Server Explorer** > **Stream Analytics** > expand the subscription and the resource group where you deployed the Edge job > you can view the MyASAEdgejob with status **Created**. Expand your job node and double-click on it to open the job view.
 
-![Server explorer options](./media/stream-analytics-tools-for-visual-studio-edge-jobs/server-explorer-options.png)
+![Server explorer job management options](./media/stream-analytics-tools-for-visual-studio-edge-jobs/server-explorer-options.png)
 
 The job view window provides you with operations such as refreshing the job, deleting the job, opening the job from Azure portal etc.
 
-![Job diagram and other options](./media/stream-analytics-tools-for-visual-studio-edge-jobs/job-diagram-and-other-options.png) 
+![Job diagram and other options in Visual Studio](./media/stream-analytics-tools-for-visual-studio-edge-jobs/job-diagram-and-other-options.png) 
 
 ## Next steps