Process & Analyze Printify Data in Databricks (AWS)

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 Printify data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Printify data in Databricks.

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

Install the CData JDBC Driver in Databricks

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

👁 Loading the JDBC JAR File into AWS

Access Printify Data in your Notebook: Python

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

Configure the Connection to Printify

Connect to Printify by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the 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.

Step 1: Connection Information

driver = "cdata.jdbc.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Printify.apip;ProfileSettings='APIKey=your_personal_token';"

Built-in Connection String Designer

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

java -jar cdata.jdbc.api.jar

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

Start by setting the Profile connection property to the location of the Printify Profile on disk (e.g. C:\profiles\Profile.apip). Next, set the ProfileSettings connection property to the connection string for Printify (see below).

Printify API Profile Settings

In order to authenticate to Printify, you'll need to provide your API Key. To get your API Key navigate to My Profile, then Connections. In the Connections section you will be able to generate your Personal Access Token (API Key) and set your Token Access Scopes. Personal Access Tokens are valid for one year. An expired Personal Access Token can be re-generated using the same steps after it expires. Set the API Key to your Personal Access Token in the ProfileSettings property to connect.

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Printify Data

Once you configure the connection, you can load Printify 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" , "Tags") \
	.load ()

Display Printify Data

Check the loaded Printify data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

👁 Displaying Printify Data

Analyze Printify 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 Printify data for reporting, visualization, and analysis.

% sql

SELECT Id, ShippingMethod FROM SAMPLE_VIEW ORDER BY ShippingMethod DESC LIMIT 5

👁 Displaying Printify Data

The data from Printify 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 API Driver for JDBC and start working with your live Printify data in Databricks. Reach out to our Support Team if you have any questions.