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

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

Install the CData JDBC Driver for Authorize.Net

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

Start a Spark Shell and Connect to Authorize.Net Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Authorize.Net/lib/cdata.jdbc.authorizenet.jar
   

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

   You can obtain the necessary connection properties on the Security Settings -> General Settings page after logging into your Merchant Account.

   • UseSandbox: The Authorize.Net API to be used to process transactions. If you are using a production account, this property can be left blank. If you are using a developer test account, set this to 'TRUE'.
   • LoginID: The API login Id associated with your payment gateway account. This property is used to authenticate that you are authorized to submit website transactions. Note that this value is not the same as the login Id that you use to log in to the Merchant Interface.
   • TransactionKey: The transaction key associated with your payment gateway account. This property is used to authenticate that you are authorized to submit website transactions.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.authorizenet.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 Authorize.Net, using the connection string generated above.

   scala> val authorizenet_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:authorizenet:LoginId=MyLoginId;TransactionKey=MyTransactionKey;").option("dbtable","SettledBatchList").option("driver","cdata.jdbc.authorizenet.AuthorizeNetDriver").load()
   

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

4. Register the Authorize.Net data as a temporary table:

   scala> authorizenet_df.registerTable("settledbatchlist")

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

   scala> authorizenet_df.sqlContext.sql("SELECT MarketType, TotalCharge FROM SettledBatchList WHERE IncludeStatistics = True").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 Authorize.Net in Apache Spark, you are able to perform fast and complex analytics on Authorize.Net 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.