Updated notebook instructions

Author: uc-msft

samples/features/sql-big-data-cluster/spark/README.md

@@ -2,6 +2,12 @@
 
 The new built-in notebooks in Azure Data Studio enables data scientists and data engineers to run Python, R, or Scala code against the cluster.
 
+## Instructions to open a notebook from Azure Data Studio
+
+1. Connect to the SQL Server Master instance in a big data cluster
+
+1. Right-click on the server name, select **Manage**, switch to **SQL Server Big Data Cluster** tab, and use open Notebook
+
 ## __[dataloading](dataloading/)__
 
 This folder contains samples that show how to load data using Spark.
@@ -10,10 +16,8 @@ This folder contains samples that show how to load data using Spark.
 
 ## Instructions
 
-1. Download and save the notebook file [dataloading/transform-csv-files.ipynb](dataloading/transform-csv-files.ipynb/) locally.
-
-1. Open the notebook file in Azure Data Studio (right click on the SQL Server big data cluster  server name-> **Manage**-> Open Notebook.
+1. Download and save the notebook file [dataloading/transnform-csv-files.ipynb](dataloading/transform-csv-files.ipynb/) locally.
 
-1. Wait for the “Kernel” and the target context (“Attach to”) to be populated. Set the “Kernel” to **PySpark3** and “Attach to” needs to be the IP address of your big data cluster endpoint.
+1. Open the notebook in Azure Data Studio, wait for the “Kernel” and the target context (“Attach to”) to be populated. Set the “Kernel” to **PySpark3** and **Attach to** needs to be the IP address of your big data cluster endpoint.
 
-1. Run each cell in the Notebook sequentially using Azure Data Studio.
+1. Run each cell in the Notebook sequentially.