Ready to get started?

Learn more about the CData JDBC Driver for Quandl or download a free trial:

Download Now

Pipe Quandl Data in Google Data Fusion

Load the CData JDBC Driver into Google Data Fusion and pipe live Quandl data to any supported data platform.

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData JDBC Driver for Quandl enables users to access live Quandl data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Quandl data to any data source natively supported in Google Data Fusion, this article walks through piping data from Quandl to Google BigQuery,

Upload the CData JDBC Driver for Quandl to Google Data Fusion

Upload the CData JDBC Driver for Quandl to your Google Data Fusion instance to work with live Quandl data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format -.jar. For example: cdata.jdbc.quandl-2019.jar

  1. Open your Google Data Fusion instance
  2. Click the to add an entity and upload a driver
  3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
  4. On the "Driver configuration" tab:
    • Name: Create a name for the driver (cdata.jdbc.quandl) and make note of the name
    • Class name: Set the JDBC class name: (cdata.jdbc.quandl.QuandlDriver)
  5. Click "Finish"

Pipe Quandl Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Quandl data in Google Data Fusion Pipelines.

  1. Navigate to the Pipeline Studio to create a new Pipeline
  2. From the "Source" options, click "Database" to add a source for the JDBC Driver
  3. Click "Properties" on the Database source to edit the properties
    • Set the Label
    • Set Reference Name to a value for any future references (i.e.: cdata-quandl)
    • Set Plugin Type to "jdbc"
    • Set Connection String to the JDBC URL for Quandl. For example:

      jdbc:quandl:5246...;APIKey=abc123;DatabaseCode=WIKI;

      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.

      To use the JDBC Driver in Google Data Fusion, you will need to set the RTK property in the JDBC URL. You can view the licensing file included in the installation for information on how to set this property.

      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.

    • Set Import Query to a SQL query that will extract the data you want from Quandl, i.e.:
      SELECT * FROM AAPL
  4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
  5. Click "Properties" on the BigQuery sink to edit the properties
    • Set the Label
    • Set Reference Name to a value like quandl-bigquery
    • Set Projcect ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
    • Set Dataset to a specific Google BigQuery dataset
    • Set Table to the name of the table you wish to insert Quandl data into

With the Source and Sink configured, you are ready to pipe Quandl data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from Quandl and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex Quandl pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData JDBC Driver for Quandl and start working with your live Quandl data in Google Data Fusion today.