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Updated notebook instructions

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Umachandar Jayachandran
2019-04-09 10:22:04 -07:00
parent ace31cdbd3
commit d5ca07afa6
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samples/features/sql-big-data-cluster/spark/README.md
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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. Download and save the notebook file [dataloading/transnform-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. 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. 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.