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Import Snowflake Data Using Azure Data Factory



Use CData Connect Cloud to connect to Snowflake from Azure Data Factory and import live Snowflake data.

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

CData Connect Cloud offers a cloud-to-cloud interface tailored for Snowflake, granting you the ability to access live data from Snowflake data 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 Cloud seamlessly channels all supported SQL operations, including filters and JOINs, directly to Snowflake. This harnesses server-side processing to expedite the retrieval of the desired Snowflake data.

Configure Snowflake Connectivity for ADF

Connectivity to Snowflake from Azure Data Factory is made possible through CData Connect Cloud. To work with Snowflake data from Azure Data Factory, we start by creating and configuring a Snowflake connection.

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

  1. Log into Connect Cloud, click Connections and click Add Connection
  2. Select "Snowflake" from the Add Connection panel
  3. Enter the necessary authentication properties to connect to Snowflake.

    To connect to Snowflake:

    1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
    2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
    3. Set Warehouse to the Snowflake warehouse.
    4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
    5. (Optional) Set Database and Schema to restrict the tables and views exposed.

    See the Getting Started guide in the CData driver documentation for more information.

  4. Click Create & Test
  5. Navigate to the Permissions tab in the Add Snowflake Connection page and update the User-based permissions.

Add a Personal Access Token

If you are connecting from a service, application, platform, or framework that does not support OAuth authentication, you can create a Personal Access Token (PAT) to use for authentication. Best practices would dictate that you create a separate PAT for each service, to maintain granularity of access.

  1. Click on your username at the top right of the Connect Cloud app and click User Profile.
  2. On the User Profile page, scroll down to the Personal Access Tokens section and click Create PAT.
  3. Give your PAT a name and click Create.
  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, you are ready to connect to Snowflake data from Azure Data Factory.

Access Live Snowflake Data in Azure Data Factory

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

  1. Login to Azure Data Factory.
  2. If you have not yet created a Data Factory, Click New -> Dataset.
  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.
  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 Cloud data source you want to connect to (for example, Snowflake1).
    • User Name - enter your CData Connect Cloud username. This is displayed in the top-right corner of the CData Connect Cloud interface. For example, test@cdata.com.
    • Password - select Password (not Azure Key Vault) and enter the PAT you generated on the Settings page.
    • Click Create.
  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.
  6. After creating the linked service, the following screen should appear:
  7. Click preview data to see the imported Snowflake table.
  8. You can now use this dataset when creating data flows in Azure Data Factory.

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