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Work with OFX Data in Apache Spark Using SQL

Access and process OFX Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for OFX, Spark can work with live OFX data. This article describes how to connect to and query OFX data from a Spark shell.

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

Install the CData JDBC Driver for OFX

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

Start a Spark Shell and Connect to OFX Data

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

    The OFXUser and OFXPassword properties, under the Authentication section, must be set to valid OFX user credentials. In addition to this, you will need to configure FIURL, FIOrganizationName, and FIID, which will be specific for the financial institution. You will also need to provide application-specific settings, including OFXVersion, ApplicationVersion, and ApplicationId.

    To connect to some services, you will need to provide additional account information such as AccountId, AccountType, BankId, BrokerId, and CCNumber.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.ofx.jar

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

    scala> val ofx_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:ofx:OFXUser=myUser;OFXPassword=myPassword;FIID=myFIID;").option("dbtable","InvBalances").option("driver","cdata.jdbc.ofx.OFXDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the OFX data as a temporary table:

    scala> ofx_df.registerTable("invbalances")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> ofx_df.sqlContext.sql("SELECT Id, Amount FROM InvBalances WHERE ServiceType = CREDITCARD").collect.foreach(println)

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

Using the CData JDBC Driver for OFX in Apache Spark, you are able to perform fast and complex analytics on OFX data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 170+ CData JDBC Drivers and get started today.