How to connect and process Shopify Data from 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 Shopify data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Shopify data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Shopify 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.shopify.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to Shopify from Databricks

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

Configure the Connection to Shopify

Connect to Shopify 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.shopify.ShopifyDriver"
url = "jdbc:shopify:RTK=5246...;AppId=MyAppId;Password=MyPassword;ShopUrl=https://yourshopname.myshopify.com;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Shopify 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 make use of all the features of the data provider, provide the AppId, Password, and ShopUrl connection properties.

To obtain these values, see the Getting Started section in the help documentation to register the data provider as an application with Shopify.

Load Shopify Data

Once the connection is configured, you can load Shopify 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" , "Customers") \
	.load ()

Display Shopify Data

Check the loaded Shopify data by calling the display function.

display (remote_table.select ("FirstName"))

Analyze Shopify 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 Shopify data for analysis.

% sql

SELECT FirstName, Id FROM Customers

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