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

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

Install the CData JDBC Driver for Klaviyo

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

Start a Spark Shell and Connect to Klaviyo Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Klaviyo/lib/cdata.jdbc.klaviyo.jar
   

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

   To authenticate to Klaviyo, provide an API Key. You can generate or view your API keys under 'My Account'

   1. Navigate to 'Settings' > 'API Keys'
   2. Click 'Create API Key'.
   3. Name your API key and choose the desired scopes.

   To connect in your CData solutions, set API Key to your Klaviyo API key.

   If you wish to use OAuth authentication, refer to the Help documenation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.klaviyo.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 Klaviyo, using the connection string generated above.

   scala> val klaviyo_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:klaviyo:APIKey=my_api_key;").option("dbtable","Campaigns").option("driver","cdata.jdbc.klaviyo.KlaviyoDriver").load()
   

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

4. Register the Klaviyo data as a temporary table:

   scala> klaviyo_df.registerTable("campaigns")

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

   scala> klaviyo_df.sqlContext.sql("SELECT Id, Name FROM Campaigns WHERE Status = draft").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 Klaviyo in Apache Spark, you are able to perform fast and complex analytics on Klaviyo 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.