Connect and Visualize Live YouTube Analytics Data in Databricks Lakehouse Federation with CData Connect AI

Databricks Lakehouse Federation enables organizations to query and integrate data from multiple sources without requiring data movement. It allows federated queries across databases, data warehouses, and lakehouses, providing a unified interface for data analysis and management within Databricks. When combined with CData Connect AI, it enables seamless access to YouTube Analytics data for data virtualization, while also supporting data lineage and fine-grained access control.

This article explains how to use CData Connect AI to establish a live connection to YouTube Analytics and how to access live YouTube Analytics data from the Databricks platform.

CData Connect AI offers a seamless SQL Server, cloud-to-cloud interface for YouTube Analytics, enabling you to effortlessly create dashboards and visualizations using live YouTube Analytics data in Databricks. While building visualizations, Databricks requires SQL queries to retrieve the necessary data. With built-in optimized data processing, CData Connect AI pushes all supported SQL operations (such as filters and JOINs) directly to YouTube Analytics, utilizing server-side processing for fast and efficient data retrieval of YouTube Analytics data.

Configure YouTube Analytics connectivity for Databricks in CData Connect AI

To work with YouTube Analytics data in Databricks - Lakehouse Federation, you need to connect to YouTube Analytics from Connect AI and provide user access to the connection.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "YouTube Analytics" from the Add Connection panel
👁 Selecting a data source

3. YouTube Analytics uses OAuth to authenticate. Click "Sign in" to authenticate with YouTube Analytics. 👁 Authenticating with OAuth (Salesforce is shown).
   
4. Navigate to the Permissions tab in the Add YouTube Analytics Connection page and update the User-based permissions. 👁 Updating permissions
   

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to YouTube Analytics data from Databricks.

Connecting live YouTube Analytics data in Databricks

Follow these steps to establish a connection from Databricks to the CData Connect AI Virtual SQL Server API.

1. Log into Databricks.
2. Navigate to SQL Warehouses and start any warehouse of your choice. 👁 Start SQL Warehouse
   
3. In the navigation pane, select Catalog. Click and select Create a connection. 👁 Create a connection
   
4. In the Connection basics section (or Step 1 of Set up connection page), enter the following connection details and click Next:
   • Connection name: a user-defined connection name.
   • Connection type: select SQL Server from the drop-down list.
   • Auth type: select Username and password.
   👁 Add connection basics details
   
5. In the Authentication section (or Step 2), enter the required authentication details, and click Next:
   • Host: tds.cdata.com
   • Port: 14333
   • User: enter your CData Connect AI username, displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
   • Password: enter the PAT generated and copied in the previous section.
   👁 Add authentication details
   
6. In the Connection details section (or Step 3), enable the Trust server certificate checkbox and select the appropriate Application intent. Click Create Connection. 👁 Add connection details
   
7. In the Catalog basics section (or Step 4), enter the required details and click Create catalog:
   • Catalog name: enter a name of your choice
   • Connection: this will be the Databricks connection you defined earlier
   • Database: enter your YouTube Analytics connection name (for example, YouTube Analytics1)
   👁 Add catalog basics details
   
8. In the Access section (or Step 5), assign the Workspace, User access rights, and Grant read or edit privileges to the catalog. 👁 Add the access rights
   👁 Grant the access rights
   
9. Click Next > Save to save all the details for the catalog. 👁 Save the catalog details and set up the connection
   

Access the catalog and visualize live YouTube Analytics data in Databricks

To access the newly created catalog and create a dashboard to visualize live YouTube Analytics data in Databricks, follow these steps:

1. Select the catalog and expand it. A list of tables from YouTube Analytics will appear on the screen. 👁 Select and expand the catalog
   
2. Choose the desired table and click the Overview tab to view the table metadata. 👁 Select Overview
   👁 View the table metadata
   
3. Click the Sample Data tab to view real-time data in the table. 👁 Select Sample Data to view the table data
   
4. Now, click Create at the top right corner and select Dashboard. 👁 Create a new dashboard
   
5. Manually create a visualization by selecting at least one field in the visualization editor from the widget, or choose one of the visualization options suggested by Databricks AI. 👁 Create the dashboard manually or using the Databricks AI
   
6. Once the visualization is created, edit the details in the widget settings of the dashboard. 👁 Visualization is created
   
7. Click Publish to publish the dashboard report. 👁 Publish the dashboard
   

Live access to YouTube Analytics data from cloud applications

At this stage, you have established a direct, cloud-to-cloud connection to live YouTube Analytics data in Databricks. This enables you to create dashboards to monitor and visualize your data seamlessly.

For more details on accessing live data from over 100 SaaS, Big Data, and NoSQL sources through cloud applications like Databricks, visit our Connect AI page. As always, let us know if you have any questions during your evaluation. Our world-class CData Support Team is always available to help!