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⇱ Import Snowflake Data Using Azure Data Factory

Import Snowflake Data Using Azure Data Factory

👁 Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Use CData Connect AI to connect to Snowflake Data 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 AI, ADF enables immediate cloud-to-cloud access to Snowflake data within data flows. This article outlines the process of connecting to Snowflake through Connect AI and accessing Snowflake data within ADF.

CData Connect AI 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 AI 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.

About Snowflake Data Integration

CData simplifies access and integration of live Snowflake data. Our customers leverage CData connectivity to:

  • Reads and write Snowflake data quickly and efficiently.
  • Dynamically obtain metadata for the specified Warehouse, Database, and Schema.
  • Authenticate in a variety of ways, including OAuth, OKTA, Azure AD, Azure Managed Service Identity, PingFederate, private key, and more.

Many CData users use CData solutions to access Snowflake from their preferred tools and applications, and replicate data from their disparate systems into Snowflake for comprehensive warehousing and analytics.

For more information on integrating Snowflake with CData solutions, refer to our blog: https://www.cdata.com/blog/snowflake-integrations.


Getting Started


Configure Snowflake Connectivity for ADF

Connectivity to Snowflake from Azure Data Factory is made possible through CData Connect AI. To work with Snowflake data from Azure Data Factory, we start by creating and configuring a Snowflake 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
    2. 👁 Adding a Connection
  2. Select "Snowflake" from the Add Connection panel
    3. 👁 Selecting a data source
  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.
    6. (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.

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

    👁 Configuring a connection (Salesforce is shown)
  4. Click Save & Test
  5. Navigate to the Permissions tab in the Add Snowflake 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 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 AI Virtual SQL Server API, follow these steps.

  1. Login to Azure Data Factory.
    2. 👁 Logging in to ADF
  2. If you have not yet created a Data Factory, Click New -> Dataset.
    3. 👁 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.
    4. 👁 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, Snowflake1).
    • 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.
    5. 👁 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.
    6. 👁 Setting the properties
  6. After creating the linked service, the following screen should appear:
    7. 👁 Displaying the new screen
  7. Click preview data to see the imported Snowflake table.
    8. 👁 Previewing the imported table
    You can now use this dataset when creating data flows in Azure Data Factory.

Get CData Connect AI

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications, try CData Connect AI today!