Process & Analyze PayPal 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 PayPal data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live PayPal data in Databricks.

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

Install the CData JDBC Driver in Databricks

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

Access PayPal Data in your Notebook: Python

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

Configure the Connection to PayPal

Connect to PayPal by referencing the JDBC Driver class 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.

Step 1: Connection Information

driver = "cdata.jdbc.paypal.PayPalDriver"
url = "jdbc:paypal:RTK=5246...;Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

The provider surfaces tables from two PayPal APIs. The APIs use different authentication methods.

• The REST API uses the OAuth standard. To authenticate to the REST API, you will need to set the OAuthClientId, OAuthClientSecret, and CallbackURL properties.
• The Classic API requires Signature API credentials. To authenticate to the Classic API, you will need to obtain an API username, password, and signature.

See the "Getting Started" chapter of the help documentation for a guide to obtaining the necessary API credentials.

To select the API you want to work with, you can set the Schema property to REST or SOAP. By default the SOAP schema will be used.

For testing purposes you can set UseSandbox to true and use sandbox credentials.

Load PayPal Data

Once you configure the connection, you can load PayPal 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" , "Transactions") \
	.load ()

Display PayPal Data

Check the loaded PayPal data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Date"))

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

% sql

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

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