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

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

Getting Started

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Install the CData JDBC Driver for HubSpot

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

Start a Spark Shell and Connect to HubSpot Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for HubSpot/lib/cdata.jdbc.hubspot.jar
   

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

   HubSpot uses the OAuth authentication standard. You can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app.

   See the Getting Started chapter of the help documentation for a guide to using OAuth.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.hubspot.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 HubSpot, using the connection string generated above.

   scala> val hubspot_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:hubspot:InitiateOAuth=GETANDREFRESH;").option("dbtable","Prospects").option("driver","cdata.jdbc.hubspot.HubSpotDriver").load()
   

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

4. Register the HubSpot data as a temporary table:

   scala> hubspot_df.registerTable("prospects")

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

   scala> hubspot_df.sqlContext.sql("SELECT Slug, PageViews FROM Prospects WHERE Region = ONTARIO").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 HubSpot in Apache Spark, you are able to perform fast and complex analytics on HubSpot 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.