How to connect Cloudinary and Microsoft SQL Server
Create a New Scenario to Connect Cloudinary and Microsoft SQL Server
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Cloudinary, triggered by another scenario, or executed manually (for testing purposes). In most cases, Cloudinary or Microsoft SQL Server will be your first step. To do this, click "Choose an app," find Cloudinary or Microsoft SQL Server, and select the appropriate trigger to start the scenario.

Add the Cloudinary Node
Select the Cloudinary node from the app selection panel on the right.


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Configure the Cloudinary
Click on the Cloudinary node to configure it. You can modify the Cloudinary URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Microsoft SQL Server Node
Next, click the plus (+) icon on the Cloudinary node, select Microsoft SQL Server from the list of available apps, and choose the action you need from the list of nodes within Microsoft SQL Server.


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Authenticate Microsoft SQL Server
Now, click the Microsoft SQL Server node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Microsoft SQL Server settings. Authentication allows you to use Microsoft SQL Server through Latenode.
Configure the Cloudinary and Microsoft SQL Server Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Cloudinary and Microsoft SQL Server Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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Save and Activate the Scenario
After configuring Cloudinary, Microsoft SQL Server, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Cloudinary and Microsoft SQL Server integration works as expected. Depending on your setup, data should flow between Cloudinary and Microsoft SQL Server (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Cloudinary and Microsoft SQL Server
Cloudinary + Airtable + Microsoft SQL Server: When a new resource is uploaded to Cloudinary, it triggers the creation of a record in Airtable to store the optimized image and corresponding metadata. The metadata is also saved to Microsoft SQL Server for reporting purposes.
Microsoft SQL Server + Cloudinary + Google Drive: When a new or updated row is detected in Microsoft SQL Server (triggering a report), images related to that row are retrieved from Cloudinary and then compiled into a report saved as a file in Google Drive.
Cloudinary and Microsoft SQL Server integration alternatives

About Cloudinary
Automate image and video optimization with Cloudinary in Latenode. Resize, convert, and deliver media assets based on triggers or data from any app. Streamline content workflows by integrating Cloudinary’s powerful transformations directly into your automated processes, reducing manual work. Scale efficiently and pay only for execution time.
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About Microsoft SQL Server
Use Microsoft SQL Server in Latenode to automate database tasks. Directly query, update, or insert data in response to triggers. Sync SQL data with other apps; simplify data pipelines for reporting and analytics. Build automated workflows without complex coding to manage databases efficiently and scale operations.
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See how Latenode works
FAQ Cloudinary and Microsoft SQL Server
How can I connect my Cloudinary account to Microsoft SQL Server using Latenode?
To connect your Cloudinary account to Microsoft SQL Server on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Cloudinary and click on "Connect".
- Authenticate your Cloudinary and Microsoft SQL Server accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automatically back up Cloudinary assets to SQL?
Yes, you can! Latenode simplifies this with visual workflows. Securely archive Cloudinary images and videos in Microsoft SQL Server, ensuring data redundancy and business continuity.
What types of tasks can I perform by integrating Cloudinary with Microsoft SQL Server?
Integrating Cloudinary with Microsoft SQL Server allows you to perform various tasks, including:
- Storing Cloudinary asset metadata in a Microsoft SQL Server database.
- Triggering image transformations in Cloudinary based on SQL data changes.
- Creating reports on Cloudinary usage using SQL Server's reporting services.
- Archiving historical Cloudinary assets to SQL Server for compliance.
- Synchronizing Cloudinary folder structures with SQL Server database tables.
How does Latenode handle large file transfers from Cloudinary?
Latenode optimizes large file transfers using streaming and chunking. This ensures efficient and reliable data movement between Cloudinary and SQL Server, even for high-resolution media.
Are there any limitations to the Cloudinary and Microsoft SQL Server integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex SQL queries might require custom JavaScript code within Latenode.
- Real-time synchronization of very large datasets can be resource-intensive.
- The number of concurrent connections is subject to your Latenode plan limits.