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

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

Install the CData JDBC Driver in Azure

To work with live Webflow data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use.
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   

Connect to Webflow from Databricks

With the JAR file installed, we are ready to work with live Webflow data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to Webflow

Connect to Webflow 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.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Webflow.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;"

Built-in Connection String Designer

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

Authentication

Webflow uses OAuth 2.0 authentication to ensure secure access to sites, CMS collections, e-commerce data, and other resources. This authentication method allows you to securely connect to your Webflow workspace and manage resources with proper authorization.

OAuth 2.0 Setup and Configuration

Step 1: Create a Webflow OAuth Application

To set up OAuth authentication:

1. Visit the Webflow Developer Portal
2. Navigate to "Apps & Integrations" in your Webflow account
3. Click "Register an App" to create a new OAuth application
4. Configure the application name, description, and redirect URI (CallbackURL)
5. Copy the Client ID and Client Secret for use in your connection

Required Connection Properties

• AuthScheme: Set this to OAuth (required)
• OAuthClientId: Client ID from your Webflow OAuth application (required)
• OAuthClientSecret: Client secret from your Webflow OAuth application (required)
• CallbackURL: Redirect URI specified in your OAuth application (required)
• InitiateOAuth: Set to GETANDREFRESH for automatic token management (recommended)

Required OAuth Scopes

The Webflow API Profile requires the following OAuth scopes:

• sites:read - Read access to site information and configuration
• pages:read - Read access to site pages
• cms:read - Read access to CMS collections and items
• forms:read - Read access to forms and form submissions
• assets:read - Read access to media assets and folders
• ecommerce:read - Read access to products, orders, and inventory
• authorized_user:read - Read access to the authorized user

Load Webflow Data

Once the connection is configured, you can load Webflow 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" , "Sites") \
	.load ()

Display Webflow Data

Check the loaded Webflow data by calling the display function.

display (remote_table.select (""))

Analyze Webflow 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 Webflow data for analysis.

result = spark.sql("SELECT ,  FROM SAMPLE_VIEW WHERE Id = 'your_site_id'")

The data from Webflow 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 Webflow data in Azure Databricks. Reach out to our Support Team if you have any questions.