Build Paddle-Connected ETL Processes in Google Data Fusion

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

Upload the CData API Driver for JDBC to Google Data Fusion

Upload the CData API Driver for JDBC to your Google Data Fusion instance to work with live Paddle 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 driver-version.jar. For example: cdataapi-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver 👁 Image
   
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.api) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.api.APIDriver)
   👁 Configuring the driver (Salesforce is shown.)
   
5. Click "Finish"

Connect to Paddle Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Paddle 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 👁 Adding a database source
   
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-api)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Paddle. For example:
     
     jdbc:api:RTK=5246...;Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";

     Using API Key Authentication

     Paddle uses API key authentication. To obtain an API key:

     1. Sign in to your Paddle account at https://vendors.paddle.com
     2. Navigate to Developer Tools > Authentication
     3. Click "Generate API Key"
     4. Assign the appropriate permissions for the data you wish to access
     5. Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)

     After obtaining your API key, set the following connection properties:

     • AuthScheme: Set this to APIKey.
     Set the following in the ProfileSettings connection property:
     • APIKey: Set this to your Paddle API key.

     Example Connection String

     Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";
     

     Connecting to Paddle

     Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.

     Built-in Connection String Designer

     For assistance in constructing the JDBC URL, use the connection string designer built into the Paddle 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.

     👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
     
   • Set Import Query to a SQL query that will extract the data you want from Paddle, i.e.:
     SELECT * FROM Products
   👁 Configuring the database source
   
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 api-bigquery
   • Set Project 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 Paddle data into
   👁 Configuring the BigQuery sink
   

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

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