Connect to Pinterest Data from HeidiSQL

Mohsin Turki
Mohsin Turki
Technical Marketing Engineer
Use CData Connect AI to connect to and query live Pinterest 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 Pinterest data. This article demonstrates how to connect to Pinterest using Connect AI and query Pinterest data in HeidiSQL.

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

Configure Pinterest Connectivity for HeidiSQL

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

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Adding a Connection
  3. Select "Pinterest" from the Add Connection panel
  4. Selecting a data source
  5. Enter the necessary authentication properties to connect to Pinterest.

    Pinterest authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Pinterest developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL.

    Set InitiateOAuth to GETANDREFRESH and set OAuthClientId, OAuthClientSecret, and CallbackURL based on the property values for the app you created.

    See the Help documentation for other OAuth authentication flows.

    Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab in the Add Pinterest 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 Pinterest data from HeidiSQL.

Connect to Pinterest 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-Pinterest
  3. 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 Pinterest from HeidiSQL

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