Connect to Salesforce Marketing 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 AI, HediSQL reach extends to include access to live Salesforce Marketing data. This article demonstrates how to connect to Salesforce Marketing using Connect AI and query Salesforce Marketing data in HeidiSQL.

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

Configure Salesforce Marketing Connectivity for HeidiSQL

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

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

2. Select "Salesforce Marketing" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Salesforce Marketing.

   Authenticating to the Salesforce Marketing Cloud APIs

   Set the and to your login credentials, or to the credentials for a sandbox user if you are connecting to a sandbox account.

   Connecting to the Salesforce Marketing Cloud APIs

   By default, the data provider connects to production environments. Set to true to use a Salesforce Marketing Cloud sandbox account.

   The default Instance is s7 of the Web Services API; however, if you use a different instance, you can set .

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Salesforce Marketing 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 Salesforce Marketing data from HeidiSQL.

Connect to Salesforce Marketing from HeidiSQL using Connect AI

To establish a connection from HeidiSQL to the CData Connect AI 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-Salesforce Marketing
👁 Creating a new session in HeidiSQL.

Configure a SQL Server Connection to Connect AI

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 AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
5. Set the Password to your PAT created in Connect AI in the previous section.
6. Set the Port to 14333 👁 Configuring a SQL Server connection to Connect AI
   

Query Salesforce Marketing from HeidiSQL

1. In the database listing on the left, find your connection to Salesforce Marketing configured earlier.
2. Expand this connection to view individual tables or data objects present within Salesforce Marketing.
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 👁 Querying within HeidiSQL.
   

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