Connect to SAS Data Sets 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 SAS Data Sets data. This article demonstrates how to connect to SAS Data Sets using Connect AI and query SAS Data Sets data in HeidiSQL.

CData Connect AI provides a pure SQL Server interface for SAS Data Sets, allowing you to query data from SAS Data Sets 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 SAS Data Sets, leveraging server-side processing to return the requested SAS Data Sets data quickly.

Configure SAS Data Sets Connectivity for HeidiSQL

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

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

2. Select "SAS Data Sets" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS Data Sets.

   Set the following connection properties to connect to your SAS DataSet files:

   Connecting to Local Files

   • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
   • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

   Connecting to Cloud-Hosted SAS DataSet Files

   While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

   Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add SAS Data Sets 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 SAS Data Sets data from HeidiSQL.

Connect to SAS Data Sets 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-SAS Data Sets

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

Query SAS Data Sets from HeidiSQL

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