Connect to Microsoft Dataverse 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 Microsoft Dataverse data. This article demonstrates how to connect to Microsoft Dataverse using Connect Cloud and query Microsoft Dataverse data in HeidiSQL.

CData Connect Cloud provides a pure SQL Server interface for Microsoft Dataverse, allowing you to query data from Microsoft Dataverse 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 Microsoft Dataverse, leveraging server-side processing to return the requested Microsoft Dataverse data quickly.

Configure Microsoft Dataverse Connectivity for HeidiSQL

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

1. Log into Connect Cloud, click Connections and click Add Connection
2. Select "Microsoft Dataverse" from the Add Connection panel
3. Enter the necessary authentication properties to connect to Microsoft Dataverse.

   You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
   • OrganizationUrl: Set this to the organization URL you are connecting to, such as https://myorganization.crm.dynamics.com.
   • Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

   When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.

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

Add a Personal Access Token

If you are connecting from a service, application, platform, or framework that does not support OAuth authentication, you can create a Personal Access Token (PAT) to use for authentication. Best practices would dictate that you create a separate PAT for each service, to maintain granularity of access.

1. Click on your username at the top right of the Connect Cloud app and click User Profile.
2. On the User Profile page, scroll down to the Personal Access Tokens section and click Create PAT.
3. Give your 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.

Connect to Microsoft Dataverse 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-Microsoft Dataverse

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 Microsoft Dataverse from HeidiSQL

1. In the database listing on the left, find your connection to Microsoft Dataverse configured earlier.
2. Expand this connection to view individual tables or data objects present within Microsoft Dataverse.
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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