Connect to Azure Analysis Services Data from HeidiSQL

HeidiSQL is an open-source database administration tool that natively supports MariaDB, MySQL, SQL Server, and PostgreSQL. When paired with CData Connect Cloud, HediSQL reach extends to include access to live Azure Analysis Services data. This article demonstrates how to connect to Azure Analysis Services using Connect Cloud and query Azure Analysis Services data in HeidiSQL.

CData Connect Cloud provides a pure SQL Server interface for Azure Analysis Services, allowing you to query data from Azure Analysis Services without replicating the data to a natively supported database. Using optimized data processing out of the box, CData Connect Cloud pushes all supported SQL operations (filters, JOINs, etc.) directly to Azure Analysis Services, leveraging server-side processing to return the requested Azure Analysis Services data quickly.

Configure Azure Analysis Services Connectivity for HeidiSQL

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

1. Log into Connect Cloud, click Sources, and then click Add Connection

2. Select "Azure Analysis Services" from the Add Connection panel

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

   To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

   Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

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

Add a Personal Access Token

When connecting to Connect Cloud 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 Cloud. 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 Cloud 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 Azure Analysis Services data from HeidiSQL.

Connect to Azure Analysis Services from HeidiSQL using Connect Cloud

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

Create a new HeidiSQL Session

1. In the Session Manager, select New in the bottom-left
2. Give the new session a descriptive name, e.g. Connect-Cloud-Azure Analysis Services

Configure a SQL Server Connection to Connect Cloud

1. In the session settings, set the Network type to Microsoft SQL Server (TCP/IP)
2. The Library DLL should automatically update to MSOLEDBSQL
3. Set the Hostname/IP to tds.cdata.com
4. Set the User to your CData Connect Cloud username. This is displayed in the top-right corner of the CData Connect Cloud interface. For example, [email protected]
5. Set the Password to your PAT created in Connect Cloud in the previous section.
6. Set the Port to 14333

Query Azure Analysis Services from HeidiSQL

1. In the database listing on the left, find your connection to Azure Analysis Services configured earlier.
2. Expand this connection to view individual tables or data objects present within Azure Analysis Services.
3. Write custom SQL queries targeting these tables, treating the data source like any SQL Server database, or visually explore each tabular data set by selecting the relevant tables

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