Process & Analyze Amazon Marketplace Data in Azure Databricks

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 Amazon Marketplace data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Amazon Marketplace data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Amazon Marketplace data in Databricks, install the driver on your Azure 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.amazonmarketplace.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to Amazon Marketplace from Databricks

With the JAR file installed, we are ready to work with live Amazon Marketplace 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 Amazon Marketplace, and create a basic report.

Configure the Connection to Amazon Marketplace

Connect to Amazon Marketplace by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.amazonmarketplace.AmazonMarketplaceDriver"
url = "jdbc:amazonmarketplace:RTK=5246...;AWS Access Key Id=myAWSAccessKeyId;AWS Secret Key=myAWSSecretKey;MWS Auth Token=myMWSAuthToken;Seller Id=mySellerId;Marketplace=United States;"

Built-in Connection String Designer

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

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

To connect to the Amazon Marketplace Webservice (MWS), AWSAccessKeyId, MWSAuthToken, AWSSecretKey and SellerId are required. You can optionally set the Marketplace property. For more information on obtaining values for these properties, refer to the Help documentation.

Load Amazon Marketplace Data

Once the connection is configured, you can load Amazon Marketplace data as a dataframe using the CData JDBC Driver and the connection information.

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

Display Amazon Marketplace Data

Check the loaded Amazon Marketplace data by calling the display function.

display (remote_table.select ("AmazonOrderId"))

Analyze Amazon Marketplace Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Amazon Marketplace data for analysis.

% sql

SELECT AmazonOrderId, OrderStatus FROM Orders WHERE IsReplacementOrder = True

The data from Amazon Marketplace 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 Amazon Marketplace and start working with your live Amazon Marketplace data in Azure Databricks. Reach out to our Support Team if you have any questions.