Import SQL Analysis Services Data 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 SQL Analysis Services data within data flows. This article outlines the process of connecting to SQL Analysis Services through Connect AI and accessing SQL Analysis Services data within ADF.

CData Connect AI offers a cloud-to-cloud interface tailored for SQL Analysis Services, granting you the ability to access live data from SQL Analysis Services 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 SQL Analysis Services. This harnesses server-side processing to expedite the retrieval of the desired SQL Analysis Services data.

Configure SQL Analysis Services Connectivity for ADF

Connectivity to SQL Analysis Services from Azure Data Factory is made possible through CData Connect AI. To work with SQL Analysis Services data from Azure Data Factory, we start by creating and configuring a SQL Analysis Services 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. Select "SQL Analysis Services" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SQL Analysis Services.

   To connect, provide authentication and set the Url property to a valid SQL Server Analysis Services endpoint. You can connect to SQL Server Analysis Services instances hosted over HTTP with XMLA access. See the Microsoft documentation to configure HTTP access to SQL Server Analysis Services.

   To secure connections and authenticate, set the corresponding connection properties, below. The data provider supports the major authentication schemes, including HTTP and Windows, as well as SSL/TLS.

   • HTTP Authentication

     Set AuthScheme to "Basic" or "Digest" and set User and Password. Specify other authentication values in CustomHeaders.

   • Windows (NTLM)

     Set the Windows User and Password and set AuthScheme to "NTLM".

   • Kerberos and Kerberos Delegation

     To authenticate with Kerberos, set AuthScheme to NEGOTIATE. To use Kerberos delegation, set AuthScheme to KERBEROSDELEGATION. If needed, provide the User, Password, and KerberosSPN. By default, the data provider attempts to communicate with the SPN at the specified Url.

   • SSL/TLS:

     By default, the data provider attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

   You can then access any cube as a relational table: When you connect the data provider retrieves SSAS metadata and dynamically updates the table schemas. Instead of retrieving metadata every connection, you can set the CacheLocation property to automatically cache to a simple file-based store.

   See the Getting Started section of the CData documentation, under Retrieving Analysis Services Data, to execute SQL-92 queries to the cubes.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add SQL Analysis Services Connection page and update the User-based 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.
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 SQL Analysis Services data from Azure Data Factory.

Access Live SQL Analysis Services 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. 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 AI data source you want to connect to (for example, SSAS1).
   • 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. 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 SQL Analysis Services table.
You can now use this dataset when creating data flows in Azure Data Factory.

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