Process & Analyze SharePoint Data in Databricks (AWS)

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SharePoint JDBC Driver

Provides Java developers with the power to easily connect their Web, Desktop, and Mobile applications to data in SharePoint Server Lists, Contacts, Calendar, Links, Tasks, and more!



Host the CData JDBC Driver for SharePoint in AWS and use Databricks to perform data engineering and data science on live SharePoint data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live SharePoint data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live SharePoint data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live SharePoint data. When you issue complex SQL queries to SharePoint, the driver pushes supported SQL operations, like filters and aggregations, directly to SharePoint and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze SharePoint data using native data types.

Install the CData JDBC Driver in Databricks

To work with live SharePoint data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.sharepoint.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for SharePoint\lib).

Access SharePoint Data in your Notebook: Python

With the JAR file installed, we are ready to work with live SharePoint data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query SharePoint, and create a basic report.

Configure the Connection to SharePoint

Connect to SharePoint by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.sharepoint.SharePointDriver"
url = "jdbc:sharepoint:User=myuseraccount;Password=mypassword;Auth Scheme=NTLM;URL=http://sharepointserver/mysite;SharePointEdition=SharePointOnPremise;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SharePoint JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.sharepoint.jar

Fill in the connection properties and copy the connection string to the clipboard.

Set the URL property to the base SharePoint site or to a sub-site. This allows you to query any lists and other SharePoint entities defined for the site or sub-site.

The User and Password properties, under the Authentication section, must be set to valid SharePoint user credentials when using SharePoint On-Premise.

If you are connecting to SharePoint Online, set the SharePointEdition to SHAREPOINTONLINE along with the User and Password connection string properties. For more details on connecting to SharePoint Online, see the "Getting Started" chapter of the help documentation

Load SharePoint Data

Once you configure the connection, you can load SharePoint data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "MyCustomList") \
	.load ()

Display SharePoint Data

Check the loaded SharePoint data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Name"))

Analyze SharePoint Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the SharePoint data for reporting, visualization, and analysis.

% sql

SELECT Name, Revenue FROM SAMPLE_VIEW ORDER BY Revenue DESC LIMIT 5

The data from SharePoint is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for SharePoint and start working with your live SharePoint data in Databricks. Reach out to our Support Team if you have any questions.