How to work with Marketo 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 Marketo, Spark can work with live Marketo data. This article describes how to connect to and query Marketo data from a Spark shell.

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

Install the CData JDBC Driver for Marketo

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

Start a Spark Shell and Connect to Marketo Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Marketo/lib/cdata.jdbc.marketo.jar
   

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

   Both the REST and SOAP APIs are supported and can be chosen by using the Schema property.

   For the REST API: The OAuthClientId, OAuthClientSecret, and RESTEndpoint properties, under the OAuth and REST Connection sections, must be set to valid Marketo user credentials.

   For the SOAP API: The UserId, EncryptionKey, and SOAPEndpoint properties, under the SOAP Connection section, must be set to valid Marketo user credentials.

   See the "Getting Started" chapter of the help documentation for a guide to obtaining these values.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.marketo.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 Marketo, using the connection string generated above.

   scala> val marketo_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:marketo:Schema=REST;RESTEndpoint=https://311-IFS-929.mktorest.com/rest;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;").option("dbtable","Leads").option("driver","cdata.jdbc.marketo.MarketoDriver").load()
   

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

4. Register the Marketo data as a temporary table:

   scala> marketo_df.registerTable("leads")

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

   scala> marketo_df.sqlContext.sql("SELECT Email, AnnualRevenue FROM Leads WHERE Country = U.S.A.").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 Marketo in Apache Spark, you are able to perform fast and complex analytics on Marketo 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.