How to connect Microsoft SQL Server and Deepgram
Create a New Scenario to Connect Microsoft SQL Server and Deepgram
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 Microsoft SQL Server, triggered by another scenario, or executed manually (for testing purposes). In most cases, Microsoft SQL Server or Deepgram will be your first step. To do this, click "Choose an app," find Microsoft SQL Server or Deepgram, and select the appropriate trigger to start the scenario.

Add the Microsoft SQL Server Node
Select the Microsoft SQL Server node from the app selection panel on the right.


Microsoft SQL Server

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


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Authenticate Deepgram
Now, click the Deepgram node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Deepgram settings. Authentication allows you to use Deepgram through Latenode.
Configure the Microsoft SQL Server and Deepgram 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 Microsoft SQL Server and Deepgram 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 Microsoft SQL Server, Deepgram, 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 Microsoft SQL Server and Deepgram integration works as expected. Depending on your setup, data should flow between Microsoft SQL Server and Deepgram (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Microsoft SQL Server and Deepgram
Deepgram + Microsoft SQL Server + Slack: When a new audio file is transcribed by Deepgram, the transcription is stored in a Microsoft SQL Server database. If specific keywords are found in the transcript (using a custom query), a Slack message is sent to a designated channel, alerting the team to potential issues.
Deepgram + Microsoft SQL Server + OpenAI ChatGPT: Deepgram transcribes audio files, and the transcription is stored in a Microsoft SQL Server database. Subsequently, OpenAI ChatGPT summarizes the transcript to aid in data analysis and identifying key themes.
Microsoft SQL Server and Deepgram integration alternatives

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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About Deepgram
Need to transcribe audio/video inside your Latenode automations? Deepgram offers fast, accurate speech-to-text. Connect it to your workflows for automated meeting summaries, content analysis, or customer support monitoring. Fine-tune results with custom vocabularies, all within Latenode's visual interface and code blocks.
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See how Latenode works
FAQ Microsoft SQL Server and Deepgram
How can I connect my Microsoft SQL Server account to Deepgram using Latenode?
To connect your Microsoft SQL Server account to Deepgram on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Microsoft SQL Server and click on "Connect".
- Authenticate your Microsoft SQL Server and Deepgram accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze call center audio stored in SQL?
Yes! Latenode lets you trigger Deepgram from database changes. Analyze transcriptions with AI blocks, updating your SQL records—all without complex coding.
What types of tasks can I perform by integrating Microsoft SQL Server with Deepgram?
Integrating Microsoft SQL Server with Deepgram allows you to perform various tasks, including:
- Transcribing audio files linked in a Microsoft SQL Server database.
- Analyzing customer sentiment from transcribed call recordings.
- Automatically updating database records with transcription insights.
- Generating reports based on voice data extracted from transcriptions.
- Monitoring audio for compliance and regulatory requirements.
How do I handle large audio files within my SQL database?
Latenode supports streaming large audio files to Deepgram. Scale effortlessly using serverless functions, avoiding memory limits of typical integrations.
Are there any limitations to the Microsoft SQL Server and Deepgram integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Database query limits depend on your Microsoft SQL Server plan.
- Deepgram transcription accuracy relies on audio quality.
- Workflow complexity can impact execution time.