Merge pull request #106655 from dagiro/freshness21

freshness21

Author: v-albemi

articles/hdinsight/spark/apache-spark-connect-to-sql-database.md

@@ -1,28 +1,28 @@
 ---
-title: Use Apache Spark to read and write data to Azure SQL database 
-description: Learn how to set up a connection between HDInsight Spark cluster and an Azure SQL database to read data, write data, and stream data into a SQL database
+title: Use Apache Spark to read and write data to Azure SQL Database 
+description: Learn how to set up a connection between HDInsight Spark cluster and an Azure SQL Database to read data, write data, and stream data into a SQL database
 author: hrasheed-msft
 ms.author: hrasheed
 ms.reviewer: jasonh
 ms.service: hdinsight
-ms.custom: hdinsightactive
 ms.topic: conceptual
-ms.date: 10/03/2019
+ms.custom: hdinsightactive
+ms.date: 03/05/2020
 ---
 
-# Use HDInsight Spark cluster to read and write data to Azure SQL database
+# Use HDInsight Spark cluster to read and write data to Azure SQL Database
 
-Learn how to connect an Apache Spark cluster in Azure HDInsight with an Azure SQL database and then read, write, and stream data into the SQL database. The instructions in this article use a [Jupyter Notebook](https://jupyter.org/) to run the Scala code snippets. However, you can create a standalone application in Scala or Python and perform the same tasks.
+Learn how to connect an Apache Spark cluster in Azure HDInsight with an Azure SQL Database and then read, write, and stream data into the SQL database. The instructions in this article use a [Jupyter Notebook](https://jupyter.org/) to run the Scala code snippets. However, you can create a standalone application in Scala or Python and perform the same tasks.
 
 ## Prerequisites
 
-* Azure HDInsight Spark cluster*.  Follow the instructions at [Create an Apache Spark cluster in HDInsight](apache-spark-jupyter-spark-sql.md).
+* Azure HDInsight Spark cluster.  Follow the instructions at [Create an Apache Spark cluster in HDInsight](apache-spark-jupyter-spark-sql.md).
 
-* Azure SQL database. Follow the instructions at [Create an Azure SQL database](../../sql-database/sql-database-get-started-portal.md). Make sure you create a database with the sample **AdventureWorksLT** schema and data. Also, make sure you create a server-level firewall rule to allow your client's IP address to access the SQL database on the server. The instructions to add the firewall rule is available in the same article. Once you've created your Azure SQL database, make sure you keep the following values handy. You need them to connect to the database from a Spark cluster.
+* Azure SQL Database. Follow the instructions at [Create an Azure SQL Database](../../sql-database/sql-database-get-started-portal.md). Make sure you create a database with the sample **AdventureWorksLT** schema and data. Also, make sure you create a server-level firewall rule to allow your client's IP address to access the SQL database on the server. The instructions to add the firewall rule is available in the same article. Once you've created your Azure SQL Database, make sure you keep the following values handy. You need them to connect to the database from a Spark cluster.
 
-    * Server name hosting the Azure SQL database.
-    * Azure SQL database name.
-    * Azure SQL database admin user name / password.
+    * Server name hosting the Azure SQL Database.
+    * Azure SQL Database name.
+    * Azure SQL Database admin user name / password.
 
 * SQL Server Management Studio (SSMS). Follow the instructions at [Use SSMS to connect and query data](../../sql-database/sql-database-connect-query-ssms.md).
 
@@ -55,11 +55,11 @@ Start by creating a [Jupyter Notebook](https://jupyter.org/) associated with the
 
 You can now start creating your application.
 
-## Read data from Azure SQL database
+## Read data from Azure SQL Database
 
 In this section, you read data from a table (for example, **SalesLT.Address**) that exists in the AdventureWorks database.
 
-1. In a new Jupyter notebook, in a code cell, paste the following snippet and replace the placeholder values with the values for your Azure SQL database.
+1. In a new Jupyter notebook, in a code cell, paste the following snippet and replace the placeholder values with the values for your Azure SQL Database.
 
        // Declare the values for your Azure SQL database
 
@@ -80,7 +80,7 @@ In this section, you read data from a table (for example, **SalesLT.Address**) t
        connectionProperties.put("user", s"${jdbcUsername}")
        connectionProperties.put("password", s"${jdbcPassword}")         
 
-1. Use the snippet below to create a dataframe with the data from a table in your Azure SQL database. In this snippet, we use a **SalesLT.Address** table that is available as part of the **AdventureWorksLT** database. Paste the snippet in a code cell and press **SHIFT + ENTER** to run.
+1. Use the snippet below to create a dataframe with the data from a table in your Azure SQL Database. In this snippet, we use a `SalesLT.Address` table that is available as part of the **AdventureWorksLT** database. Paste the snippet in a code cell and press **SHIFT + ENTER** to run.
 
        val sqlTableDF = spark.read.jdbc(jdbc_url, "SalesLT.Address", connectionProperties)
 
@@ -100,11 +100,11 @@ In this section, you read data from a table (for example, **SalesLT.Address**) t
 
        sqlTableDF.select("AddressLine1", "City").show(10)
 
-## Write data into Azure SQL database
+## Write data into Azure SQL Database
 
-In this section, we use a sample CSV file available on the cluster to create a table in Azure SQL database and populate it with data. The sample CSV file (**HVAC.csv**) is available on all HDInsight clusters at `HdiSamples/HdiSamples/SensorSampleData/hvac/HVAC.csv`.
+In this section, we use a sample CSV file available on the cluster to create a table in Azure SQL Database and populate it with data. The sample CSV file (**HVAC.csv**) is available on all HDInsight clusters at `HdiSamples/HdiSamples/SensorSampleData/hvac/HVAC.csv`.
 
-1. In a new Jupyter notebook, in a code cell, paste the following snippet and replace the placeholder values with the values for your Azure SQL database.
+1. In a new Jupyter notebook, in a code cell, paste the following snippet and replace the placeholder values with the values for your Azure SQL Database.
 
        // Declare the values for your Azure SQL database
 
@@ -135,17 +135,17 @@ In this section, we use a sample CSV file available on the cluster to create a t
        readDf.createOrReplaceTempView("temphvactable")
        spark.sql("create table hvactable_hive as select * from temphvactable")
 
-1. Finally, use the hive table to create a table in Azure SQL database. The following snippet creates `hvactable` in Azure SQL database.
+1. Finally, use the hive table to create a table in Azure SQL Database. The following snippet creates `hvactable` in Azure SQL Database.
 
        spark.table("hvactable_hive").write.jdbc(jdbc_url, "hvactable", connectionProperties)
 
-1. Connect to the Azure SQL database using SSMS and verify that you see a `dbo.hvactable` there.
+1. Connect to the Azure SQL Database using SSMS and verify that you see a `dbo.hvactable` there.
 
-    a. Start SSMS and connect to the Azure SQL database by providing connection details as shown in the screenshot below.
+    a. Start SSMS and connect to the Azure SQL Database by providing connection details as shown in the screenshot below.
 
     ![Connect to SQL database using SSMS1](./media/apache-spark-connect-to-sql-database/connect-to-sql-db-ssms.png "Connect to SQL database using SSMS1")
 
-    b. From **Object Explorer**, expand the Azure SQL database and the Table node to see the **dbo.hvactable** created.
+    b. From **Object Explorer**, expand the Azure SQL Database and the Table node to see the **dbo.hvactable** created.
 
     ![Connect to SQL database using SSMS2](./media/apache-spark-connect-to-sql-database/connect-to-sql-db-ssms-locate-table.png "Connect to SQL database using SSMS2")
 
@@ -155,11 +155,11 @@ In this section, we use a sample CSV file available on the cluster to create a t
     SELECT * from hvactable
     ```
 
-## Stream data into Azure SQL database
+## Stream data into Azure SQL Database
 
-In this section, we stream data into the **hvactable** that you already created in Azure SQL database in the previous section.
+In this section, we stream data into the `hvactable` that you already created in Azure SQL Database in the previous section.
 
-1. As a first step, make sure there are no records in the **hvactable**. Using SSMS, run the following query on the table.
+1. As a first step, make sure there are no records in the `hvactable`. Using SSMS, run the following query on the table.
 
     ```sql
     TRUNCATE TABLE [dbo].[hvactable]
@@ -173,17 +173,17 @@ In this section, we stream data into the **hvactable** that you already created
        import org.apache.spark.sql.streaming._
        import java.sql.{Connection,DriverManager,ResultSet}
 
-1. We stream data from the **HVAC.csv** into the hvactable. HVAC.csv file is available on the cluster at `/HdiSamples/HdiSamples/SensorSampleData/HVAC/`. In the following snippet, we first get the schema of the data to be streamed. Then, we create a streaming dataframe using that schema. Paste the snippet in a code cell and press **SHIFT + ENTER** to run.
+1. We stream data from the **HVAC.csv** into the `hvactable`. HVAC.csv file is available on the cluster at `/HdiSamples/HdiSamples/SensorSampleData/HVAC/`. In the following snippet, we first get the schema of the data to be streamed. Then, we create a streaming dataframe using that schema. Paste the snippet in a code cell and press **SHIFT + ENTER** to run.
 
        val userSchema = spark.read.option("header", "true").csv("wasbs:///HdiSamples/HdiSamples/SensorSampleData/hvac/HVAC.csv").schema
        val readStreamDf = spark.readStream.schema(userSchema).csv("wasbs:///HdiSamples/HdiSamples/SensorSampleData/hvac/") 
        readStreamDf.printSchema
 
-1. The output shows the schema of **HVAC.csv**. The **hvactable** has the same schema as well. The output lists the columns in the table.
+1. The output shows the schema of **HVAC.csv**. The `hvactable` has the same schema as well. The output lists the columns in the table.
 
     ![hdinsight Apache Spark schema table](./media/apache-spark-connect-to-sql-database/hdinsight-schema-table.png "Schema of table")
 
-1. Finally, use the following snippet to read data from the HVAC.csv and stream it into the **hvactable** in Azure SQL database. Paste the snippet in a code cell, replace the placeholder values with the values for your Azure SQL database, and then press **SHIFT + ENTER** to run.
+1. Finally, use the following snippet to read data from the HVAC.csv and stream it into the `hvactable` in Azure SQL Database. Paste the snippet in a code cell, replace the placeholder values with the values for your Azure SQL Database, and then press **SHIFT + ENTER** to run.
 
        val WriteToSQLQuery  = readStreamDf.writeStream.foreach(new ForeachWriter[Row] {
           var connection:java.sql.Connection = _
@@ -224,7 +224,7 @@ In this section, we stream data into the **hvactable** that you already created
 
          var streamingQuery = WriteToSQLQuery.start()
 
-1. Verify that the data is being streamed into the **hvactable** by running the following query in SQL Server Management Studio (SSMS). Every time you run the query, it shows the number of rows in the table increasing.
+1. Verify that the data is being streamed into the `hvactable` by running the following query in SQL Server Management Studio (SSMS). Every time you run the query, it shows the number of rows in the table increasing.
 
     ```sql
     SELECT COUNT(*) FROM hvactable