diff --git a/samples/features/sql-big-data-cluster/spark/README.md b/samples/features/sql-big-data-cluster/spark/README.md
index b81b1b59..e2d7ac00 100644
--- a/samples/features/sql-big-data-cluster/spark/README.md
+++ b/samples/features/sql-big-data-cluster/spark/README.md
@@ -1,17 +1,8 @@
 # SQL Server big data clusters
 
-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.
+SQL Server Big Data cluster bundles Spark and HDFS together with SQL server. Azure Data Studio IDE provides built in notebooks that enables data scientists and data engineers to run Spark notebooks and job in Python, R, or Scala code against the Big Data Cluster. This folder contains spark sample notebook on using Spark in SQL server Big data 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/)__
-<<<<<<< HEAD
-
-This folder contains samples that show how to load data using Spark.
+## Folder contents
 
 [PySpark Hello World](dataloading/hello_PySpark.ipynb)
 
@@ -22,23 +13,13 @@ This folder contains samples that show how to load data using Spark.
 [DataLoading   - Transforming CSV to Parquet](dataloading/transform-csv-files.ipynb/)
 
 [Data Transfer - Spark to SQL using JDBC ](spark_to_sql/spark_to_sql_jdbc.ipynb/)
-=======
 
-This folder contains samples that show how to load data using Spark.
-
-[dataloading/transform-csv-files.ipynb](dataloading/transform-csv-files.ipynb/)
->>>>>>> upstream/master
-
-## Instructions
+## Instructions on how to run in Azure Data Studio
 
 1. Download and save the notebook file [dataloading/transnform-csv-files.ipynb](dataloading/transform-csv-files.ipynb/) locally.
 
-<<<<<<< HEAD
-2. 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.
+2. From Azure Data Studio Connect to the SQL Server Master instance in a big data cluster. 
 
-3. Run each cell in the Notebook sequentially.
-=======
-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.
+3. Right-click on the server name, select **Manage**, switch to **SQL Server Big Data Cluster** tab, and open the notebook in Azure Data Studio.  Wait for the “Kernel” and the target context (“Attach to”) to be populated. If required set the relevant “Kernel” ( e.g **PySpark3** )  and **Attach to** needs to be the IP address of your big data cluster endpoint.
 
-1. Run each cell in the Notebook sequentially.
->>>>>>> upstream/master
+4. Run each cell in the Notebook sequentially.