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

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

Install the CData JDBC Driver for BigCommerce

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

Start a Spark Shell and Connect to BigCommerce Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for BigCommerce JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for BigCommerce/lib/cdata.jdbc.bigcommerce.jar
2. With the shell running, you can connect to BigCommerce with a JDBC URL and use the SQL Context load() function to read a table.

   BigCommerce authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Big Commerce developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL. These three parameters will be set as connection properties to your driver.

   Additionally, in order to connect to your BigCommerce Store, you will need your StoreId. To find your Store Id please follow these steps:

   1. Log in to your BigCommerce account.
   2. From the Home Page, select Advanced Settings > API Accounts.
   3. Click Create API Account.
   4. A text box named API Path will appear on your screen.
   5. Inside you can see a URL of the following structure: https://api.bigcommerce.com/stores/{Store Id}/v3.
   6. As demonstrated above, your Store Id will be between the 'stores/' and '/v3' path paramters.
   7. Once you have retrieved your Store Id you can either click Cancel or proceed in creating an API Account in case you do not have one already.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.bigcommerce.jar

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

   Configure the connection to BigCommerce, using the connection string generated above.

   scala> val bigcommerce_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:bigcommerce:OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333'").option("dbtable","Customers").option("driver","cdata.jdbc.bigcommerce.BigCommerceDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the BigCommerce data as a temporary table:

   scala> bigcommerce_df.registerTable("customers")

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

   scala> bigcommerce_df.sqlContext.sql("SELECT FirstName, LastName FROM Customers WHERE FirstName = Bob").collect.foreach(println)

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

Using the CData JDBC Driver for BigCommerce in Apache Spark, you are able to perform fast and complex analytics on BigCommerce data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.