Connect and Visualize Live SQL Analysis Services Data in Databricks Lakehouse Federation with CData Connect AI

Databricks Lakehouse Federation enables organizations to query and integrate data from multiple sources without requiring data movement. It allows federated queries across databases, data warehouses, and lakehouses, providing a unified interface for data analysis and management within Databricks. When combined with CData Connect AI, it enables seamless access to SQL Analysis Services data for data virtualization, while also supporting data lineage and fine-grained access control.

This article explains how to use CData Connect AI to establish a live connection to SQL Analysis Services and how to access live SQL Analysis Services data from the Databricks platform.

CData Connect AI offers a seamless SQL Server, cloud-to-cloud interface for SQL Analysis Services, enabling you to effortlessly create dashboards and visualizations using live SQL Analysis Services data in Databricks. While building visualizations, Databricks requires SQL queries to retrieve the necessary data. With built-in optimized data processing, CData Connect AI pushes all supported SQL operations (such as filters and JOINs) directly to SQL Analysis Services, utilizing server-side processing for fast and efficient data retrieval of SQL Analysis Services data.

Configure SQL Analysis Services connectivity for Databricks in CData Connect AI

To work with SQL Analysis Services data in Databricks - Lakehouse Federation, you need to connect to SQL Analysis Services from Connect AI and provide user access to the connection.

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 Databricks.

Connecting live SQL Analysis Services data in Databricks

Follow these steps to establish a connection from Databricks to the CData Connect AI Virtual SQL Server API.

1. Log into Databricks.
2. Navigate to SQL Warehouses and start any warehouse of your choice.
3. In the navigation pane, select Catalog. Click and select Create a connection.
4. In the Connection basics section (or Step 1 of Set up connection page), enter the following connection details and click Next:
   • Connection name: a user-defined connection name.
   • Connection type: select SQL Server from the drop-down list.
   • Auth type: select Username and password.
   
5. In the Authentication section (or Step 2), enter the required authentication details, and click Next:
   • Host: tds.cdata.com
   • Port: 14333
   • User: enter your CData Connect AI username, displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
   • Password: enter the PAT generated and copied in the previous section.
   
6. In the Connection details section (or Step 3), enable the Trust server certificate checkbox and select the appropriate Application intent. Click Create Connection.
7. In the Catalog basics section (or Step 4), enter the required details and click Create catalog:
   • Catalog name: enter a name of your choice
   • Connection: this will be the Databricks connection you defined earlier
   • Database: enter your SQL Analysis Services connection name (for example, SQL Analysis Services1)
   
8. In the Access section (or Step 5), assign the Workspace, User access rights, and Grant read or edit privileges to the catalog.
9. Click Next > Save to save all the details for the catalog.

Access the catalog and visualize live SQL Analysis Services data in Databricks

To access the newly created catalog and create a dashboard to visualize live SQL Analysis Services data in Databricks, follow these steps:

1. Select the catalog and expand it. A list of tables from SQL Analysis Services will appear on the screen.
2. Choose the desired table and click the Overview tab to view the table metadata.
3. Click the Sample Data tab to view real-time data in the table.
4. Now, click Create at the top right corner and select Dashboard.
5. Manually create a visualization by selecting at least one field in the visualization editor from the widget, or choose one of the visualization options suggested by Databricks AI.
6. Once the visualization is created, edit the details in the widget settings of the dashboard.
7. Click Publish to publish the dashboard report.

Live access to SQL Analysis Services data from cloud applications

At this stage, you have established a direct, cloud-to-cloud connection to live SQL Analysis Services data in Databricks. This enables you to create dashboards to monitor and visualize your data seamlessly.

For more details on accessing live data from over 100 SaaS, Big Data, and NoSQL sources through cloud applications like Databricks, visit our Connect AI page. As always, let us know if you have any questions during your evaluation. Our world-class CData Support Team is always available to help!