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Work with Quandl Data in Apache Spark Using SQL

Access and process Quandl 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 Quandl, Spark can work with live Quandl data. This article describes how to connect to and query Quandl data from a Spark shell.

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

Install the CData JDBC Driver for Quandl

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

Start a Spark Shell and Connect to Quandl Data

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

    Quandl uses an API key for authentication. See the help documentation for a guide to obtaining the APIKey property.

    Additionally, set the DatabaseCode connection property to the code identifying the Database whose Datasets you want to query with SQL. You can search the available Databases by querying the Databases view.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.quandl.jar

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

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

    scala> val quandl_df ="jdbc").option("url", "jdbc:quandl:APIKey=abc123;DatabaseCode=WIKI;").option("dbtable","AAPL").option("driver","cdata.jdbc.quandl.QuandlDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Quandl data as a temporary table:

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

    scala> quandl_df.sqlContext.sql("SELECT Date, Volume FROM AAPL WHERE Collapse = Daily").collect.foreach(println)

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

Using the CData JDBC Driver for Quandl in Apache Spark, you are able to perform fast and complex analytics on Quandl 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.