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

Add the Google Cloud Speech-To-Text Node
Select the Google Cloud Speech-To-Text node from the app selection panel on the right.

Google Cloud Speech-To-Text
Configure the Google Cloud Speech-To-Text
Click on the Google Cloud Speech-To-Text node to configure it. You can modify the Google Cloud Speech-To-Text 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 Google Cloud Speech-To-Text 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 Google Cloud Speech-To-Text 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 Google Cloud Speech-To-Text 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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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Cloud Speech-To-Text, 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 Google Cloud Speech-To-Text and Microsoft SQL Server integration works as expected. Depending on your setup, data should flow between Google Cloud Speech-To-Text 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 Google Cloud Speech-To-Text and Microsoft SQL Server
Google Cloud Speech-To-Text + Microsoft SQL Server + Slack: Transcribes audio from Google Cloud Storage using Speech-to-Text, inserts the transcription into a Microsoft SQL Server database, and notifies a Slack channel when specific keywords are found in the transcription.
Google Cloud Speech-To-Text + Jira + Microsoft SQL Server: Upon recognizing long audio and extracting its text from Google Cloud Storage, it creates a Jira ticket and then stores the audio transcript, along with the Jira ticket ID, within a Microsoft SQL Server database for future reference and tracking purposes.
Google Cloud Speech-To-Text and Microsoft SQL Server integration alternatives
About Google Cloud Speech-To-Text
Automate audio transcription using Google Cloud Speech-To-Text within Latenode. Convert audio files to text and use the results to populate databases, trigger alerts, or analyze customer feedback. Latenode provides visual tools to manage the flow, plus code options for custom parsing or filtering. Scale voice workflows without complex coding.
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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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FAQ Google Cloud Speech-To-Text and Microsoft SQL Server
How can I connect my Google Cloud Speech-To-Text account to Microsoft SQL Server using Latenode?
To connect your Google Cloud Speech-To-Text account to Microsoft SQL Server on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud Speech-To-Text and click on "Connect".
- Authenticate your Google Cloud Speech-To-Text and Microsoft SQL Server accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I store call center transcriptions in SQL database?
Yes, you can! Latenode's visual editor simplifies data transformation. Store Google Cloud Speech-To-Text transcriptions in Microsoft SQL Server for analysis and reporting.
What types of tasks can I perform by integrating Google Cloud Speech-To-Text with Microsoft SQL Server?
Integrating Google Cloud Speech-To-Text with Microsoft SQL Server allows you to perform various tasks, including:
- Storing transcribed audio data directly into a database for future analysis.
- Creating searchable archives of customer support call transcripts.
- Automating data entry from voice notes into structured SQL tables.
- Generating reports on frequently mentioned keywords from voice data.
- Building a real-time dashboard of speech-based data insights.
Can Latenode process audio files from cloud storage services?
Yes, Latenode supports processing audio files from various cloud storage services. Use our file parsing tools for advanced workflows.
Are there any limitations to the Google Cloud Speech-To-Text and Microsoft SQL Server integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large audio files might require significant processing time.
- The accuracy of speech-to-text depends on audio quality.
- SQL Server database size limits can impact long-term storage.