Analyze Live Azure Data Lake Storage Data in SAS Viya

SAS Viya is an analytics platform that enhances data management, machine learning, and analytics, fostering efficient decision-making and insights. When paired with CData Connect AI, you get instant, cloud-to-cloud access to Azure Data Lake Storage data for building predictive models, crafting stunning insights to make data-driven decisions, and more. This article shows how to connect to Connect AI from the SAS Viya cloud platform and integrate live Azure Data Lake Storage data into your self-service AI and analytics deployments.

CData Connect AI provides a pure SQL, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily integrate with live Azure Data Lake Storage data in SAS Viya — without replicating the data. CData Connect AI looks exactly like a SQL Server database to SAS Viya and uses optimized data processing out of the box to push all supported SQL operations (filters, JOINs, etc.) directly to Azure Data Lake Storage, leveraging server-side processing to return Azure Data Lake Storage data quickly.

Configure Azure Data Lake Storage Connectivity for SAS Viya

Connectivity to Azure Data Lake Storage from SAS Viya is made possible through CData Connect AI. To work with Azure Data Lake Storage data from SAS Viya, we start by creating and configuring a Azure Data Lake Storage connection.

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

2. Select "Azure Data Lake Storage" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage 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 Azure Data Lake Storage data from SAS Viya.

Connecting to CData Connect AI from SAS Viya

The following steps detail the process of loading data from Azure Data Lake Storage into SAS Viya using the established connection in CData Connect AI.

1. Download and install the CData Connect AI JDBC driver.
   • Open the Integrations page of CData Connect AI.
   • Search for and select JDBC.
   • Download and run the setup file.
   • When the installation is complete, the JAR file can be found in the installation directory (inside the lib folder).
2. Now, log in to SAS Viya and navigate to the Applications Menu at the top-left corner.
3. Select Develop Code and Flows from the Analytics Life Cycle topic. 👁 Select Develop Code and Flows
   
4. Navigate to the Explorer tab and click on SAS Server on the left panel.
5. Follow the steps to upload the JAR file of the CData Connect JDBC driver:
   • Right-click on the "Home" directory.
   • Click on Upload files.
   • Place the JAR file in the specified location and note its file path. 👁 Click on Upload files to upload the JAR file in the Home folder
     👁 JAR file uploaded
     👁 Save the location of the JAR file from File Properties
     
6. Once done, navigate to the Libraries tab and click on Create a new library connection (on the top left corner as shown below) for the CData Connect JDBC. 👁 Navigate to the Libraries tab and click on Create a new library connection
   
7. Enter the library connection settings:
   • Connection name: enter a name for your connection
   • Library name (libref): enter a reference for your library
   • Library type: choose "SAS/ACCESS to JDBC"
8. Click on the Properties tab and set Library attributes to READONLY. 👁 Click on the Properties tab and set Library attributes to READONLY
   
9. Click the Connection Options tab and enter the following details:
   • Hive JDBC driver's class name: cdata.jdbc.connect.ConnectDriver
   • Java CLASSPATH: enter the file path to the JAR driver file (Refer to Step 5) 👁 Fill Hive JDBC driver's class name
     👁 Fill Java CLASSPATH
     
10. Click on Test connection. If it succeeds, click on Save and connect. 👁 Successful test connection
    
11. Click on to add a new tab and select SAS program. 👁 Select SAS program to write SQL Queries
    
12. Fill in the code block below with your setup parameters:
    • Libref: enter the library reference you defined in Step 9.
    • ClassPath: enter the file path to the JAR driver file.
    • Username: enter your CData Connect username. This is displayed in the top-right corner of the CData Connect interface. For example, [email protected].
    • DefaultCatalog: enter the connection configured in CData Connect AI that you want to query.
    • Password: enter the PAT you generated in the "Add a Personal Access Token" section.

    libname [Libref] JDBC 
    classpath=[ClassPath] 
    class="cdata.jdbc.connect.ConnectDriver" 
    URL="jdbc:Connect:AuthScheme=Basic;User=[Username];DefaultCatalog=[DefaultCatalog];DefaultSchema=dbo;Password=[PAT]"; 
     
    proc sql; 
    
    SELECT * FROM [Libref].MyTable; 
    
    quit; 
    

13. Click on Run. You can see the data load from CData Connect AI into SAS Viya.

Live Access to Azure Data Lake Storage Data from Cloud Applications

At this point, you have a direct, cloud-to-cloud connection to Azure Data Lake Storage data from SAS Viya. You can build predictive models, craft insights to make data-driven decisions, and more — all without replicating Azure Data Lake Storage data.

Try Connect AI and get real-time data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications.