updates

Author: julieMSFT

articles/synapse-analytics/get-started.md

@@ -103,7 +103,9 @@ Once your Synapse workspace is created, you have two ways to open Synapse Studio
     |---|---|---|
     |**Apache Spark pool name**|`Spark1`
     |**Node size**| `Small`|
-    |**Number of nodes**| Set the minimum to 3 and the maximum to 3
+    |**Number of nodes**| Set the minimum to 3 and the maximum to 3|
+    |||
+
 * Select **Review+create** and then select **Create**.
 * Your Apache Spark pool will be ready in a few seconds.
 
@@ -116,6 +118,7 @@ Once your Synapse workspace is created, you have two ways to open Synapse Studio
 > Spark databases are independently created from Spark pools. A workspace always has a Spark DB called **default** and you can create additional Spark databases.
 
 ## SQL on-demand pools
+
 SQL on-demand is a special kind of SQL pool that is always available with a Synapse workspace. It allows you to work with SQL without having to create or think about managing a Synapse SQL pool.
 
 > [!NOTE]
@@ -208,7 +211,8 @@ We have data available in a SQL pool database. Now we load it into a Spark datab
 ## Customize data visualization data with Spark and notebooks
 
 With spark notebooks you can control exactly how render charts. The following
-code shows a simple example using the popular libraries matplotlib and sea-born.
+code shows a simple example using the popular libraries matplotlib and sea-born. It will 
+render the same chart you saw when running the SQL queries earlier.
 
 ```py
 %%pyspark
@@ -223,7 +227,12 @@ seaborn.lineplot(x="PassengerCount", y="AvgTripDistance" , data = df)
 matplotlib.pyplot.show()
 ```
     
-## Load data from a Spark table into a SQL Pool table
+## Load data from a Spark table into a SQL pool table
+
+Earlier we copied data from a SQL pool database into a Spark DB. Using
+Spark, we aggregated the data into the nyctaxi.passengercountstats. 
+Now run the cell below in a notebook and it will copy the aggregated table back into
+the SQL pool datbase.
 
 ```scala
 %%spark
@@ -244,7 +253,7 @@ df.write.sqlanalytics("SQLDB1.dbo.PassengerCountStats", Constants.INTERNAL )
     ```
 
 * Select **Run**
-* NOTE: THe first time you run this it will take about 10 seconds for SQL on-demand to gather SQL resources needed to run your queries. Every subsequent query will not require this time.
+* NOTE: THe first time you run this it will take about 10 seconds for SQL on-demand to gather SQL resources needed to run your queries. Subsequent queries will not require this time.
   
 ## Use pipeline to orchestrate activities
 

articles/synapse-analytics/spark/synapse-spark-sql-pool-import-export.md

@@ -38,14 +38,14 @@ For this reason, there is no need to create credentials or specify them in the c
 
 To create users, connect to the database, and follow these examples:
 
-```Sql
+```sql
 CREATE USER Mary FROM LOGIN Mary;
 CREATE USER [mike@contoso.com] FROM EXTERNAL PROVIDER;
 ```
 
 To assign a role:
 
-```Sql
+```sql
 EXEC sp_addrolemember 'db_exporter', 'Mary';
 ```
 
@@ -58,28 +58,28 @@ The import statements are not required, they are pre-imported for the notebook e
 > [!NOTE]
 > **Imports not needed in notebook experience**
 
-```Scala
+```scala
  import com.microsoft.spark.sqlanalytics.utils.Constants
  import org.apache.spark.sql.SqlAnalyticsConnector._
 ```
 
 #### Read API
 
-```Scala
+```scala
 val df = spark.read.sqlanalytics("[DBName].[Schema].[TableName]")
 ```
 
 The above API will work for both Internal (Managed) as well as External Tables in the SQL pool.
 
 #### Write API
 
-```Scala
+```scala
 df.write.sqlanalytics("[DBName].[Schema].[TableName]", [TableType])
 ```
 
 where TableType can be Constants.INTERNAL or Constants.EXTERNAL
 
-```Scala
+```scala
 df.write.sqlanalytics("[DBName].[Schema].[TableName]", Constants.INTERNAL)
 df.write.sqlanalytics("[DBName].[Schema].[TableName]", Constants.EXTERNAL)
 ```
@@ -91,22 +91,22 @@ The authentication to Storage and the SQL Server is done
 > [!NOTE]
 > Imports not needed in notebook experience
 
-```Scala
+```scala
  import com.microsoft.spark.sqlanalytics.utils.Constants
  import org.apache.spark.sql.SqlAnalyticsConnector._
 ```
 
 #### Read API
 
-```Scala
+```scala
 val df = spark.read.
 option(Constants.SERVER, "samplews.database.windows.net").
 sqlanalytics("<DBName>.<Schema>.<TableName>")
 ```
 
 #### Write API
 
-```Scala
+```scala
 df.write.
 option(Constants.SERVER, "[samplews].[database.windows.net]").
 sqlanalytics("[DBName].[Schema].[TableName]", [TableType])
@@ -118,7 +118,7 @@ sqlanalytics("[DBName].[Schema].[TableName]", [TableType])
 
 Currently the connector doesn't support token-based auth to a SQL pool that is outside of the workspace. You'll need to use SQL Auth.
 
-```Scala
+```scala
 val df = spark.read.
 option(Constants.SERVER, "samplews.database.windows.net").
 option(Constants.USER, [SQLServer Login UserName]).
@@ -128,7 +128,7 @@ sqlanalytics("<DBName>.<Schema>.<TableName>")
 
 #### Write API
 
-```Scala
+```scala
 df.write.
 option(Constants.SERVER, "[samplews].[database.windows.net]").
 option(Constants.USER, [SQLServer Login UserName]).
@@ -145,7 +145,7 @@ Assume you have a dataframe "pyspark_df" that you want to write into the DW.
 
 Create a temp table using the dataframe in PySpark:
 
-```python
+```py
 pyspark_df.createOrReplaceTempView("pysparkdftemptable")
 ```