Process & Analyze Quandl Data in Databricks (AWS)

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Quandl JDBC Driver

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Host the CData JDBC Driver for Quandl in AWS and use Databricks to perform data engineering and data science on live Quandl data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Quandl data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Quandl data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Quandl data. 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 client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Quandl data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Quandl data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.quandl.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Quandl\lib).

Access Quandl Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Quandl data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Quandl, and create a basic report.

Configure the Connection to Quandl

Connect to Quandl by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.quandl.QuandlDriver"
url = "jdbc:quandl:APIKey=abc123;DatabaseCode=WIKI;"

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.

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.

Load Quandl Data

Once you configure the connection, you can load Quandl data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "AAPL") \
	.load ()

Display Quandl Data

Check the loaded Quandl data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Date"))

Analyze Quandl Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Quandl data for reporting, visualization, and analysis.

% sql

SELECT Date, Volume FROM SAMPLE_VIEW ORDER BY Volume DESC LIMIT 5

The data from Quandl is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Quandl and start working with your live Quandl data in Databricks. Reach out to our Support Team if you have any questions.