Resolved review comments

Author: Sreekanth Iyer (Ushta Te Consultancy Services)

articles/hdinsight/hdinsight-apps-install-custom-applications.md

@@ -101,13 +101,13 @@ If an application installation failed, you can see the error messages and debug
 
     Open the cluster from the portal, and select Applications from Settings:
 
-    :::image type="content" source="./media/hdinsight-apps-install-custom-applications/hdinsight-apps-error.png" alt-text="hdinsight application installation error.":::
+    :::image type="content" source="./media/hdinsight-apps-install-custom-applications/hdinsight-apps-error.png" alt-text=" Screenshot of HDInsight application installation error.":::
 
 * HDInsight script action: If the HDInsight Applications' error message indicates a script action failure, more details about the script failure will be presented in the script actions pane.
 
     Select Script Action from Settings. Script action history shows the error messages
 
-    :::image type="content" source="./media/hdinsight-apps-install-custom-applications/hdinsight-apps-script-action-error.png" alt-text="hdinsight applications script action error.":::
+    :::image type="content" source="./media/hdinsight-apps-install-custom-applications/hdinsight-apps-script-action-error.png" alt-text=" Screenshot of HDInsight applications script action error.":::
 
 * Apache Ambari Web UI: If the install script was the cause of the failure, use Ambari Web UI to check full logs about the install scripts.
 

articles/hdinsight/hdinsight-hadoop-create-linux-clusters-azure-powershell.md

@@ -42,7 +42,7 @@ The following script demonstrates how to create a new cluster:
 
 The values you specify for the cluster login are used to create the Hadoop user account for the cluster. Use this account to connect to services hosted on the cluster such as web UIs or REST APIs.
 
-The values you specify for the SSH user are used to create the SSH user for the cluster. Use this account to start a remote SSH session on the cluster and run jobs. For more information, see [Use SSH with HDInsight](hdinsight-hadoop-linux-use-ssh-unix.md) document.
+The values you specify for the SSH user are used to create the SSH user for the cluster. Use this account to start a remote SSH session on the cluster and run jobs. For more information, see [Use SSH with HDInsight](hdinsight-hadoop-linux-use-ssh-unix.md).
 
 > [!IMPORTANT]  
 > If you plan to use more than 32 worker nodes (either at cluster creation or by scaling the cluster after creation), you must also specify a head node size with at least 8 cores and 14 GB of RAM.

articles/hdinsight/hdinsight-operationalize-data-pipeline.md

@@ -70,7 +70,7 @@ Your Azure SQL Database is now ready.
 
 ### Provision an Apache Hadoop Cluster
 
-Create an Apache Hadoop cluster with a custom metastore. During cluster creation from the portal, from the **Storage** tab, ensures you select your SQL Database under **Metastore settings**. For more information on selecting a metastore, see [Select a custom metastore during cluster creation](./hdinsight-use-external-metadata-stores.md#select-a-custom-metastore-during-cluster-creation). For more information on cluster creation, see [Get Started with HDInsight on Linux](hadoop/apache-hadoop-linux-tutorial-get-started.md).
+Create an Apache Hadoop cluster with a custom metastore. During cluster creation from the portal, from the **Storage** tab, ensure you select your SQL Database under **Metastore settings**. For more information on selecting a metastore, see [Select a custom metastore during cluster creation](./hdinsight-use-external-metadata-stores.md#select-a-custom-metastore-during-cluster-creation). For more information on cluster creation, see [Get Started with HDInsight on Linux](hadoop/apache-hadoop-linux-tutorial-get-started.md).
 
 ## Verify SSH tunneling set up
 
@@ -159,7 +159,7 @@ The sample data is now available. However, the pipeline requires two Hive tables
 
 5. Select **Execute** to create the table.
 
-    :::image type="content" source="./media/hdinsight-operationalize-data-pipeline/hdi-ambari-services-hive-query.png" alt-text="HDInsight Ambari services hive query.":::
+    :::image type="content" source="./media/hdinsight-operationalize-data-pipeline/hdi-ambari-services-hive-query.png" alt-text="Screebshot of HDInsight Ambari services hive query.":::
 
 6. To create the `flights` table, replace the text in the query text area with the following statements. The `flights` table is a Hive-managed table that partitions data loaded into it by year, month, and day of month. This table will contain all historical flight data, with the lowest granularity present in the source data of one row per flight.
 
@@ -499,7 +499,7 @@ As you can see, the majority of the coordinator is just passing configuration in
     <coordinator-app ... start="2017-01-01T00:00Z" end="2017-01-05T00:00Z" frequency="${coord:days(1)}" ...>
     ```
 
-    A coordinator is responsible for scheduling actions within the `start` and `end` date range, according to the interval specified by the `frequency` attribute. Each scheduled action in turn runs the workflow as configured. In the coordinator definition above, the coordinator is configured to run actions from January 1, 2017 to January 5, 2017. The frequency is set to one day by the [Oozie Expression Language](https://oozie.apache.org/docs/4.2.0/CoordinatorFunctionalSpec.html#a4.4._Frequency_and_Time-Period_Representation) frequency expression `${coord:days(1)}`. This results in the coordinator scheduling an action (and hence the workflow) once per day. For date ranges that are in the past, as in this example, the action will be scheduled to run without delay. The start of the date from which an action is scheduled to run is call the *nominal time*. For example, to process the data for January 1, 2017 the coordinator will schedule action with a nominal time of 2017-01-01T00:00:00 GMT.
+    A coordinator is responsible for scheduling actions within the `start` and `end` date range, according to the interval specified by the `frequency` attribute. Each scheduled action in turn runs the workflow as configured. In the coordinator definition above, the coordinator is configured to run actions from January 1, 2017 to January 5, 2017. The frequency is set to one day by the [Oozie Expression Language](https://oozie.apache.org/docs/4.2.0/CoordinatorFunctionalSpec.html#a4.4._Frequency_and_Time-Period_Representation) frequency expression `${coord:days(1)}`. This results in the coordinator scheduling an action (and hence the workflow) once per day. For date ranges that are in the past, as in this example, the action will be scheduled to run without delay. The start of the date from which an action is scheduled to run is called the *nominal time*. For example, to process the data for January 1, 2017 the coordinator will schedule action with a nominal time of 2017-01-01T00:00:00 GMT.
 
 * Point 2: Within the date range of the workflow, the `dataset` element specifies where to look in HDFS for the data for a particular date range, and configures how Oozie determines whether the data is available yet for processing.
 

articles/hdinsight/interactive-query/apache-hive-warehouse-connector.md

@@ -120,7 +120,7 @@ Apart from the configurations mentioned in the previous section, add the followi
 
     * Use [ssh command](../hdinsight-hadoop-linux-use-ssh-unix.md) to connect to your Interactive Query cluster. Look for `default_realm` parameter in the `/etc/krb5.conf` file. Replace `<AAD-DOMAIN>` with this value as an uppercase string, otherwise the credential won't be found.
 
-        :::image type="content" source="./media/apache-hive-warehouse-connector/aad-domain.png" alt-text="hive warehouse connector AAD Domain." border="true":::
+        :::image type="content" source="./media/apache-hive-warehouse-connector/aad-domain.png" alt-text="Screenshot of Hive warehouse connector AAD Domain." border="true":::
 
     * For instance, `hive/hn*.mjry42ikpruuxgs2qy2kpg4q5e.cx.internal.cloudapp.net@PKRSRVUQVMAE6J85.D2.INTERNAL.CLOUDAPP.NET`.
 
@@ -172,7 +172,7 @@ This is a way to run Spark interactively through a modified version of the Scala
 
 Spark-submit is a utility to submit any Spark program (or job) to Spark clusters.
 
-The spark-submited job will set up and configure Spark and Hive Warehouse Connector as per our instructions, execute the program we pass to it, then cleanly release the resources that were being used.
+The spark-submit job will set up and configure Spark and Hive Warehouse Connector as per our instructions, execute the program we pass to it, then cleanly release the resources that were being used.
 
 Once you build the scala/java code along with the dependencies into an assembly jar, use the below command to launch a Spark application. Replace `<VERSION>`, and `<APP_JAR_PATH>` with the actual values.