How to work with LinkedIn Ads Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for LinkedIn Ads, Spark can work with live LinkedIn Ads data. This article describes how to connect to and query LinkedIn Ads data from a Spark shell.

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

Install the CData JDBC Driver for LinkedIn Ads

Download the CData JDBC Driver for LinkedIn Ads installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to LinkedIn Ads Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for LinkedIn Ads JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for LinkedIn Ads/lib/cdata.jdbc.linkedinads.jar
   

2. With the shell running, you can connect to LinkedIn Ads with a JDBC URL and use the SQL Context load() function to read a table.

   LinkedIn Ads uses the OAuth authentication standard. OAuth requires the authenticating user to interact with LinkedIn using the browser. See the OAuth section in the Help documentation for a guide.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.linkedinads.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to LinkedIn Ads, using the connection string generated above.

   scala> val linkedinads_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:linkedinads:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;").option("dbtable","Analytics").option("driver","cdata.jdbc.linkedinads.LinkedInAdsDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the LinkedIn Ads data as a temporary table:

   scala> linkedinads_df.registerTable("analytics")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> linkedinads_df.sqlContext.sql("SELECT VisibilityCode, Comment FROM Analytics WHERE EntityId = 238").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for LinkedIn Ads in Apache Spark, you are able to perform fast and complex analytics on LinkedIn Ads data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.