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Process & Analyze LinkedIn Data in Azure Databricks

Host the CData JDBC Driver for LinkedIn in Azure and use Databricks to perform data engineering and data science on live LinkedIn 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 LinkedIn data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live LinkedIn data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live LinkedIn data. When you issue complex SQL queries to LinkedIn, the driver pushes supported SQL operations, like filters and aggregations, directly to LinkedIn 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 LinkedIn data using native data types.

Install the CData JDBC Driver in Azure

To work with live LinkedIn data in Databricks, install the driver on your Azure 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.linkedin.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for LinkedIn\lib).

Connect to Salesforce from Databricks

With the JAR file installed, we are ready to work with live LinkedIn 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 LinkedIn, and create a basic report.

Configure the Connection to LinkedIn

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

driver = "cdata.jdbc.linkedin.LinkedInDriver"
url = "jdbc:linkedin:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.linkedin.jar

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

LinkedIn uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with LinkedIn. For more information refer to our authentication guide.

Load LinkedIn Data

Once the connection is configured, you can load LinkedIn data as a dataframe using the CData JDBC Driver and the connection information.

remote_table = ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "CompanyStatusUpdates") \
	.load ()

Display LinkedIn Data

Check the loaded LinkedIn data by calling the display function.

display ( ("VisibilityCode"))

Analyze LinkedIn Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the LinkedIn data for analysis.

% sql

SELECT VisibilityCode, Comment FROM CompanyStatusUpdates

The data from LinkedIn 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 LinkedIn and start working with your live LinkedIn data in Apache NiFi. Reach out to our Support Team if you have any questions.