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

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

Install the CData JDBC Driver for Customer.io

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

Start a Spark Shell and Connect to Customer.io Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Customer.io/lib/cdata.jdbc.api.jar
   

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

   Using API Key Authentication

   To obtain your Customer.io App API Key, navigate to the Customer.io UI under Data & Integrations > Integrations > Customer.io API and generate your API key.

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Customer.io App API Key.

   Example Connection String

   Profile=C:\profiles\CustomerIO.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";
   

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.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 Customer.io, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\CustomerIO.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";").option("dbtable","Customers").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Customer.io data as a temporary table:

   scala> api_df.registerTable("customers")

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

   scala> api_df.sqlContext.sql("SELECT , FROM Customers WHERE = ").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 Customer.io in Apache Spark, you are able to perform fast and complex analytics on Customer.io 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.