Merge pull request #50809 from JasonWHowell/quotes2

Ja-Dunn · web-flow · commit b3484f8c3673 · 2018-09-04T14:36:58.000-07:00
Replacing smart quotes with regular quotes
diff --git a/articles/hdinsight/hadoop/apache-hadoop-dotnet-avro-serialization.md b/articles/hdinsight/hadoop/apache-hadoop-dotnet-avro-serialization.md
@@ -32,7 +32,7 @@ The <a href="https://hadoopsdk.codeplex.com/wikipage?title=Avro%20Library" targe
 The serialized representation of an object in the Avro system consists of two parts: schema and actual value. The Avro schema describes the language-independent data model of the serialized data with JSON. It is presented side by side with a binary representation of data. Having the schema separate from the binary representation permits each object to be written with no per-value overheads, making serialization fast, and the representation small.
 
 ## The Hadoop scenario
-The Apache Avro serialization format is widely used in Azure HDInsight and other Apache Hadoop environments. Avro provides a convenient way to represent complex data structures within a Hadoop MapReduce job. The format of Avro files (Avro object container file) has been designed to support the distributed MapReduce programming model. The key feature that enables the distribution is that the files are “splittable” in the sense that one can seek any point in a file and start reading from a particular block.
+The Apache Avro serialization format is widely used in Azure HDInsight and other Apache Hadoop environments. Avro provides a convenient way to represent complex data structures within a Hadoop MapReduce job. The format of Avro files (Avro object container file) has been designed to support the distributed MapReduce programming model. The key feature that enables the distribution is that the files are "splittable" in the sense that one can seek any point in a file and start reading from a particular block.
 
 ## Serialization in Avro Library
 The .NET Library for Avro supports two ways of serializing objects:
diff --git a/articles/hdinsight/hdinsight-for-vscode.md b/articles/hdinsight/hdinsight-for-vscode.md
@@ -270,7 +270,7 @@ HDInsight Tools for VS Code also enables you to submit interactive PySpark queri
 After you submit a Python job, submission logs appear in the **OUTPUT** window in VS Code. The **Spark UI URL** and **Yarn UI URL** are shown as well. You can open the URL in a web browser to track the job status.
 
 >[!NOTE]
->PySpark3 is not supported anymore in Livy 0.4 (which is HDI spark 2.2 cluster). Only “PySpark” is supported for python. It is known issue that submit to spark 2.2 fail with python3.
+>PySpark3 is not supported anymore in Livy 0.4 (which is HDI spark 2.2 cluster). Only "PySpark" is supported for python. It is known issue that submit to spark 2.2 fail with python3.
    
 ## Livy configuration
 Livy configuration is supported, it could be set at the project settings in work space folder. More details, see [Livy README](https://github.com/cloudera/livy/blob/master/README.rst ).
diff --git a/articles/hdinsight/hdinsight-hadoop-r-scaler-sparkr.md b/articles/hdinsight/hdinsight-hadoop-r-scaler-sparkr.md
@@ -326,7 +326,7 @@ joinedDF5 <- rename(joinedDF4,
 
 ## Save results to CSV for exchange with ScaleR
 
-That completes the joins we need to do with SparkR. We save the data from the final Spark DataFrame “joinedDF5” to a CSV for input to ScaleR and then close out the SparkR session. We explicitly tell SparkR to save the resultant CSV in 80 separate partitions to enable sufficient parallelism in ScaleR processing:
+That completes the joins we need to do with SparkR. We save the data from the final Spark DataFrame "joinedDF5" to a CSV for input to ScaleR and then close out the SparkR session. We explicitly tell SparkR to save the resultant CSV in 80 separate partitions to enable sufficient parallelism in ScaleR processing:
 
 ```
 logmsg('output the joined data from Spark to CSV') 
diff --git a/articles/hdinsight/r-server/r-server-hdinsight-manage.md b/articles/hdinsight/r-server/r-server-hdinsight-manage.md
@@ -62,7 +62,7 @@ The following screenshot shows the outputs.
 
 ![Concurrent user 3](./media/r-server-hdinsight-manage/concurrent-users-2.png)
 
-When prompted for “Current Kerberos password:”, just press **Enter** to ignore it. The `-m` option in `useradd` command indicates that the system will create a home folder for the user, which is required for RStudio Community version.
+When prompted for "Current Kerberos password:", just press **Enter** to ignore it. The `-m` option in `useradd` command indicates that the system will create a home folder for the user, which is required for RStudio Community version.
 
 ### Step 3: Use RStudio Community version with the user created
 
diff --git a/articles/hdinsight/spark/apache-spark-jupyter-notebook-kernels.md b/articles/hdinsight/spark/apache-spark-jupyter-notebook-kernels.md
@@ -66,7 +66,7 @@ Here are a few benefits of using the new kernels with Jupyter notebook on Spark
    
    Instead, you can directly use the preset contexts in your application.
 
-- **Cell magics**. The PySpark kernel provides some predefined “magics”, which are special commands that you can call with `%%` (for example, `%%MAGIC` <args>). The magic command must be the first word in a code cell and allow for multiple lines of content. The magic word should be the first word in the cell. Adding anything before the magic, even comments, causes an error.     For more information on magics, see [here](http://ipython.readthedocs.org/en/stable/interactive/magics.html).
+- **Cell magics**. The PySpark kernel provides some predefined "magics", which are special commands that you can call with `%%` (for example, `%%MAGIC` <args>). The magic command must be the first word in a code cell and allow for multiple lines of content. The magic word should be the first word in the cell. Adding anything before the magic, even comments, causes an error.     For more information on magics, see [here](http://ipython.readthedocs.org/en/stable/interactive/magics.html).
    
     The following table lists the different magics available through the kernels.
 
diff --git a/articles/stream-analytics/stream-analytics-build-an-iot-solution-using-stream-analytics.md b/articles/stream-analytics/stream-analytics-build-an-iot-solution-using-stream-analytics.md
@@ -28,7 +28,7 @@ After completing this solution, you are able to:
 You need the following prerequisites to complete this solution:
 * An [Azure subscription](https://azure.microsoft.com/pricing/free-trial/)
 
-## Scenario introduction: “Hello, Toll!”
+## Scenario introduction: "Hello, Toll!"
 A toll station is a common phenomenon. You encounter them on many expressways, bridges, and tunnels across the world. Each toll station has multiple toll booths. At manual booths, you stop to pay the toll to an attendant. At automated booths, a sensor on top of each booth scans an RFID card that's affixed to the windshield of your vehicle as you pass the toll booth. It is easy to visualize the passage of vehicles through these toll stations as an event stream over which interesting operations can be performed.
 
 ![Picture of cars at toll booths](media/stream-analytics-build-an-iot-solution-using-stream-analytics/image1.jpg)
@@ -106,7 +106,7 @@ Here is a short description of the columns:
 ## Set up the environment for Azure Stream Analytics
 To complete this solution, you need a Microsoft Azure subscription. If you do not have an Azure account, you can [request a free trial version](http://azure.microsoft.com/pricing/free-trial/).
 
-Be sure to follow the steps in the “Clean up your Azure account” section at the end of this article so that you can make the best use of your Azure credit.
+Be sure to follow the steps in the "Clean up your Azure account" section at the end of this article so that you can make the best use of your Azure credit.
 
 ## Deploy the sample 
 There are several resources that can easily be deployed in a resource group together with a few clicks. The solution definition is hosted in github repository at [https://github.com/Azure/azure-stream-analytics/tree/master/Samples/TollApp](https://github.com/Azure/azure-stream-analytics/tree/master/Samples/TollApp).
diff --git a/articles/stream-analytics/stream-analytics-common-troubleshooting-issues.md b/articles/stream-analytics/stream-analytics-common-troubleshooting-issues.md
@@ -21,7 +21,7 @@ ms.date: 04/12/2018
 
 ![Inputs tile](media/stream-analytics-malformed-events/inputs_tile.png)
 
-To see more information, enable the diagnostics logs to view the details of the warning. For malformed input events, the execution logs contain an entry with the message that looks like: “Message: Could not deserialize the input event(s) from resource <blob URI> as json". 
+To see more information, enable the diagnostics logs to view the details of the warning. For malformed input events, the execution logs contain an entry with the message that looks like: "Message: Could not deserialize the input event(s) from resource <blob URI> as json". 
 
 ### Troubleshooting steps
 
diff --git a/articles/stream-analytics/stream-analytics-compatibility-level.md b/articles/stream-analytics/stream-analytics-compatibility-level.md
@@ -18,7 +18,7 @@ Compatibility level makes sure that existing jobs run without any failure. When
  
 ## Set a compatibility level 
 
-Compatibility level controls the runtime behavior of a stream analytics job. You can set the compatibility level for a Stream Analytics job by using portal or by using the [create job REST API call](https://docs.microsoft.com/rest/api/streamanalytics/stream-analytics-job). Azure Stream Analytics currently supports two compatibility levels- “1.0” and “1.1”. By default, the compatibility level is set to “1.0” which was introduced during general availability of Azure Stream Analytics. To update the default value, navigate to your existing Stream Analytics job > select the **Compatibility Level** option in **Configure** section and change the value. 
+Compatibility level controls the runtime behavior of a stream analytics job. You can set the compatibility level for a Stream Analytics job by using portal or by using the [create job REST API call](https://docs.microsoft.com/rest/api/streamanalytics/stream-analytics-job). Azure Stream Analytics currently supports two compatibility levels- "1.0" and "1.1". By default, the compatibility level is set to "1.0" which was introduced during general availability of Azure Stream Analytics. To update the default value, navigate to your existing Stream Analytics job > select the **Compatibility Level** option in **Configure** section and change the value. 
 
 Make sure that you stop the job before updating the compatibility level. You can’t update the compatibility level if your job is in a running state. 
 
@@ -35,11 +35,11 @@ The following major changes are introduced in compatibility level 1.1:
 
   * **previous versions:** Azure Stream Analytics used DataContractSerializer, so the message content included XML tags. For example:
     
-   @\u0006string\b3http://schemas.microsoft.com/2003/10/Serialization/\u0001{ “SensorId”:”1”, “Temperature”:64\}\u0001 
+   @\u0006string\b3http://schemas.microsoft.com/2003/10/Serialization/\u0001{ "SensorId":"1", "Temperature":64\}\u0001 
 
   * **current version:** The message content contains the stream directly with no additional tags. For example:
   
-   { “SensorId”:”1”, “Temperature”:64} 
+   { "SensorId":"1", "Temperature":64} 
  
 * **Persisting case-sensitivity for field names**  
 
diff --git a/articles/stream-analytics/stream-analytics-define-outputs.md b/articles/stream-analytics/stream-analytics-define-outputs.md
@@ -143,7 +143,7 @@ Once you have the Power BI account authenticated, you can configure the properti
 | Property name | description |
 | --- | --- |
 | Output alias |A friendly name used in queries to direct the query output to this PowerBI output. |
-| Group workspace |To enable sharing data with other Power BI users you can select groups inside your Power BI account or choose “My Workspace” if you do not want to write to a group.  Updating an existing group requires renewing the Power BI authentication. |
+| Group workspace |To enable sharing data with other Power BI users you can select groups inside your Power BI account or choose "My Workspace" if you do not want to write to a group.  Updating an existing group requires renewing the Power BI authentication. |
 | Dataset name |Provide a dataset name that it is desired for the Power BI output to use |
 | Table name |Provide a table name under the dataset of the Power BI output. Currently, Power BI output from Stream Analytics jobs can only have one table in a dataset |
 
@@ -167,7 +167,7 @@ bigint | Int64
 nvarchar(max) | String
 datetime | Datetime
 float | Double
-Record array | String type, Constant value “IRecord” or “IArray”
+Record array | String type, Constant value "IRecord" or "IArray"
 
 ### Schema Update
 Stream Analytics infers the data model schema based on the first set of events in the output. Later, if necessary, the data model schema is updated to accommodate incoming events that may not fit into the original schema.
diff --git a/articles/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices.md b/articles/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices.md
@@ -81,7 +81,7 @@ The simplest form of query is a pass-through query that archives all input data
     ![Test results](./media/stream-analytics-get-started-with-iot-devices/stream-analytics-get-started-with-iot-devices-07.png)
 
 ### Query: Filter the data based on a condition
-Let’s try to filter the results based on a condition. We would like to show results for only those events that come from “sensorA.” The query is in the Filtering.txt file.
+Let’s try to filter the results based on a condition. We would like to show results for only those events that come from "sensorA." The query is in the Filtering.txt file.
 
 ![Filtering a data stream](./media/stream-analytics-get-started-with-iot-devices/stream-analytics-get-started-with-iot-devices-08.png)
 
diff --git a/articles/stream-analytics/stream-analytics-how-to-configure-azure-machine-learning-endpoints-in-stream-analytics.md b/articles/stream-analytics/stream-analytics-how-to-configure-azure-machine-learning-endpoints-in-stream-analytics.md
@@ -64,7 +64,7 @@ Example request body:
 ````
 
 ## Call RetrieveDefaultDefinition endpoint for default UDF
-Once the skeleton UDF is created the complete definition of the UDF is needed. The RetreiveDefaultDefinition endpoint helps you get the default definition for a scalar function that is bound to an Azure Machine Learning endpoint. The payload below requires you to get the default UDF definition for a scalar function that is bound to an Azure Machine Learning endpoint. It doesn’t specify the actual endpoint as it has already been provided during PUT request. Stream Analytics calls the endpoint provided in the request if it is provided explicitly. Otherwise it uses the one originally referenced. Here the UDF takes a single string parameter (a sentence) and returns a single output of type string which indicates the “sentiment” label for that sentence.
+Once the skeleton UDF is created the complete definition of the UDF is needed. The RetreiveDefaultDefinition endpoint helps you get the default definition for a scalar function that is bound to an Azure Machine Learning endpoint. The payload below requires you to get the default UDF definition for a scalar function that is bound to an Azure Machine Learning endpoint. It doesn’t specify the actual endpoint as it has already been provided during PUT request. Stream Analytics calls the endpoint provided in the request if it is provided explicitly. Otherwise it uses the one originally referenced. Here the UDF takes a single string parameter (a sentence) and returns a single output of type string which indicates the "sentiment" label for that sentence.
 
 ````
 POST : /subscriptions/<subscriptionId>/resourceGroups/<resourceGroup>/providers/Microsoft.StreamAnalytics/streamingjobs/<streamingjobName>/functions/<udfName>/RetrieveDefaultDefinition?api-version=<apiVersion>
diff --git a/articles/stream-analytics/stream-analytics-javascript-user-defined-aggregates.md b/articles/stream-analytics/stream-analytics-javascript-user-defined-aggregates.md
@@ -74,7 +74,7 @@ Each JavaScript UDA is defined by a Function object declaration. Following are t
 
 ### Function alias
 
-Function alias is the UDA identifier. When called in Stream Analytics query, always use UDA alias together with a “uda.” prefix.
+Function alias is the UDA identifier. When called in Stream Analytics query, always use UDA alias together with a "uda." prefix.
 
 ### Function type
 
@@ -163,13 +163,13 @@ Now let’s create a JavaScript UDA under an existing ASA job by following steps
     }
     ````
 
-1. Once you click the “Save” button, your UDA shows up on the function list.
+1. Once you click the "Save" button, your UDA shows up on the function list.
 
-1. Click on the new function “TWA”, you can check the function definition.
+1. Click on the new function "TWA", you can check the function definition.
 
 ## Calling JavaScript UDA in ASA query
 
-In Azure portal and open your job, edit the query and call TWA() function with a mandate prefix “uda.”. For example:
+In Azure portal and open your job, edit the query and call TWA() function with a mandate prefix "uda.". For example:
 
 ````SQL
 WITH value AS
diff --git a/articles/stream-analytics/stream-analytics-manage-job.md b/articles/stream-analytics/stream-analytics-manage-job.md
@@ -58,15 +58,15 @@ Before Stream Analytics can analyze the fraudulent calls data stream, you should
    ![Create event hub namespace](media/stream-analytics-manage-job/create-ehns.png)
 
 5. When the namespace has finished deploying, go to **All resources** > find "myEventHubNS" in the list of Azure resources > select to open it.  
-6. Next select **+Event Hub** > **Name** the event hub “MyEventHub”. You can use a different name. Use default options on remaining settings, select **Create** and wait for the deployment to succeed.
+6. Next select **+Event Hub** > **Name** the event hub "MyEventHub". You can use a different name. Use default options on remaining settings, select **Create** and wait for the deployment to succeed.
 
    ![Create event hub](media/stream-analytics-manage-job/create-eh.png)
 
 ### Grant access to the event hub and get a connection string
 
 Before an application can send data to Azure Event Hubs, the event hub must have a policy that allows appropriate access. The access policy produces a connection string that includes authorization information.
 
-1. Navigate to the **Event Hubs** you created in the previous step that is “MyEventHub” > select **Shared access policies** from the event hub pane > select **+Add**.  
+1. Navigate to the **Event Hubs** you created in the previous step that is "MyEventHub" > select **Shared access policies** from the event hub pane > select **+Add**.  
 2. Set the Policy name to **Mypolicy** > and select **Manage** > select **Create**.  
 
    ![Create event hub shared access policy](media/stream-analytics-manage-job/create-ehpolicy.png)
@@ -237,7 +237,7 @@ You can test a query from the query editor and you need sample data to test a qu
 
 2. Select **Now** for job output start time and select **Start**. The job starts in few minutes and you can view the status in the notification bar.  
 
-3. After the job succeeds, navigate 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. Navigate to the **Datasets** tab, you can view a dataset named “ASAdataset”.  
+3. After the job succeeds, navigate 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. Navigate to the **Datasets** tab, you can view a dataset named "ASAdataset".  
 
 4. From your workspace, select **+Create**. Create a new dashboard and name it Fraudulent Calls. You will add two tiles to this dashboard, where one tile is used to view the count of fraudulent calls at a given instance and the other tile has a line chart visualization.  
 
diff --git a/articles/stream-analytics/stream-analytics-monitor-and-manage-jobs-use-powershell.md b/articles/stream-analytics/stream-analytics-monitor-and-manage-jobs-use-powershell.md
@@ -36,7 +36,7 @@ Azure PowerShell 1.0:
         Connect-AzureRmAccount
 
         # Select the Azure subscription you want to use to create the resource group.
-        Get-AzureRmSubscription –SubscriptionName “your sub” | Select-AzureRmSubscription
+        Get-AzureRmSubscription –SubscriptionName "your sub" | Select-AzureRmSubscription
 
         # If Stream Analytics has not been registered to the subscription, remove remark symbol below (#) to run the Register-AzureProvider cmdlet to register the provider namespace.
         #Register-AzureRmResourceProvider -Force -ProviderNamespace 'Microsoft.StreamAnalytics'
diff --git a/articles/stream-analytics/stream-analytics-scale-jobs.md b/articles/stream-analytics/stream-analytics-scale-jobs.md
diff --git a/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md b/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md
diff --git a/articles/stream-analytics/stream-analytics-select-into.md b/articles/stream-analytics/stream-analytics-select-into.md
diff --git a/articles/stream-analytics/stream-analytics-tools-for-visual-studio-edge-jobs.md b/articles/stream-analytics/stream-analytics-tools-for-visual-studio-edge-jobs.md
diff --git a/articles/stream-analytics/stream-analytics-troubleshooting-guide.md b/articles/stream-analytics/stream-analytics-troubleshooting-guide.md
diff --git a/articles/stream-analytics/stream-analytics-use-reference-data.md b/articles/stream-analytics/stream-analytics-use-reference-data.md
diff --git a/articles/stream-analytics/stream-analytics-with-azure-functions.md b/articles/stream-analytics/stream-analytics-with-azure-functions.md
