Import Google Search Results Using Azure Data Factory

Microsoft Azure Data Factory (ADF) is a completely managed, serverless data integration service. When combined with CData Connect AI, ADF enables immediate cloud-to-cloud access to Google Search results within data flows. This article outlines the process of connecting to Google Search through Connect AI and accessing Google Search results within ADF.

CData Connect AI offers a cloud-to-cloud interface tailored for Google Search, granting you the ability to access live data from Google Search results within Azure Data Factory without the need for data replication to a natively supported database. Equipped with optimized data processing capabilities by default, CData Connect AI seamlessly channels all supported SQL operations, including filters and JOINs, directly to Google Search. This harnesses server-side processing to expedite the retrieval of the desired Google Search results.

Configure Google Search Connectivity for ADF

Connectivity to Google Search from Azure Data Factory is made possible through CData Connect AI. To work with Google Search results from Azure Data Factory, we start by creating and configuring a Google Search connection.

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

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

2. Select "Google Search" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Google Search.

   To search with a Google custom search engine, you need to set the CustomSearchId and ApiKey connection properties.

   To obtain the CustomSearchId property, sign into Google Custom Search Engine and create a new search engine.

   To obtain the ApiKey property, you must enable the Custom Search API in the Google API Console.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Google Search 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 Google Search results from Azure Data Factory.

Access Live Google Search Results in Azure Data Factory

To establish a connection from Azure Data Factory to the CData Connect AI Virtual SQL Server API, follow these steps.

1. Login to Azure Data Factory.
👁 Logging in to ADF

2. If you have not yet created a Data Factory, Click New -> Dataset.
👁 Creating new data factory

3. In the search bar, enter SQL Server and select it when it appears. On the following screen, enter a name for the server. In the Linked service field, select New.
👁 Selecting SQL Server

4. Enter the connection settings.
   • Name - enter a name of your choice.
   • Server name - enter the Virtual SQL Server endpoint and port separated by a comma: tds.cdata.com,14333
   • Database name - enter the Connection Name of the CData Connect AI data source you want to connect to (for example, GoogleSearch1).
   • User Name - enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
   • Password - select Password (not Azure Key Vault) and enter the PAT you generated on the Settings page.
   • Click Create.
👁 Configuring new linked service

5. In Set properties, set the Name, choose the Linked service we just created, select a Table name from those available, and Import schema from connection/store. Click OK.
👁 Setting the properties

6. After creating the linked service, the following screen should appear:
👁 Displaying the new screen

7. Click preview data to see the imported Google Search table.
👁 Previewing the imported table
You can now use this dataset when creating data flows in Azure Data Factory.

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