How to connect Google Cloud Speech-To-Text and Google Cloud BigQuery
Create a New Scenario to Connect Google Cloud Speech-To-Text and Google Cloud BigQuery
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 Google Cloud BigQuery will be your first step. To do this, click "Choose an app," find Google Cloud Speech-To-Text or Google Cloud BigQuery, 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 Google Cloud BigQuery Node
Next, click the plus (+) icon on the Google Cloud Speech-To-Text node, select Google Cloud BigQuery from the list of available apps, and choose the action you need from the list of nodes within Google Cloud BigQuery.

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Authenticate Google Cloud BigQuery
Now, click the Google Cloud BigQuery node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Cloud BigQuery settings. Authentication allows you to use Google Cloud BigQuery through Latenode.
Configure the Google Cloud Speech-To-Text and Google Cloud BigQuery 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 Google Cloud BigQuery 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 Google Cloud Speech-To-Text, Google Cloud BigQuery, 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 Google Cloud BigQuery integration works as expected. Depending on your setup, data should flow between Google Cloud Speech-To-Text and Google Cloud BigQuery (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 Google Cloud BigQuery
Google Cloud Speech-To-Text + Google Cloud BigQuery + Slack: Transcribe audio files from storage using Google Cloud Speech-To-Text, then analyze the transcripts and store the results in Google Cloud BigQuery. Finally, send a message to a Slack channel based on the analysis.
Google Cloud BigQuery + Google Cloud Speech-To-Text + Google Drive: Analyze call center data in BigQuery, then use transcriptions from Google Cloud Speech-To-Text to create summaries. Save those summaries as text files to a specified folder in Google Drive.
Google Cloud Speech-To-Text and Google Cloud BigQuery 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 Google Cloud BigQuery
Use Google Cloud BigQuery in Latenode to automate data warehousing tasks. Query, analyze, and transform huge datasets as part of your workflows. Schedule data imports, trigger reports, or feed insights into other apps. Automate complex analysis without code and scale your insights with Latenode’s flexible, pay-as-you-go platform.
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FAQ Google Cloud Speech-To-Text and Google Cloud BigQuery
How can I connect my Google Cloud Speech-To-Text account to Google Cloud BigQuery using Latenode?
To connect your Google Cloud Speech-To-Text account to Google Cloud BigQuery 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 Google Cloud BigQuery accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze call center conversations for sentiment?
Yes, you can! Latenode allows automated sentiment analysis through Speech-To-Text transcription stored directly in BigQuery. Scale your analysis with Latenode’s built-in JavaScript and AI tools.
What types of tasks can I perform by integrating Google Cloud Speech-To-Text with Google Cloud BigQuery?
Integrating Google Cloud Speech-To-Text with Google Cloud BigQuery allows you to perform various tasks, including:
- Transcribe audio files and store the text data in BigQuery.
- Analyze customer support call transcripts for key trends.
- Build dashboards to visualize speech data from various sources.
- Create automated reports from audio data stored in BigQuery.
- Process and analyze meeting recordings for action items.
Can I filter Speech-To-Text results before storing in BigQuery?
Yes, you can filter results! Use Latenode's visual editor or JavaScript blocks to filter by confidence level before BigQuery storage.
Are there any limitations to the Google Cloud Speech-To-Text and Google Cloud BigQuery integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large audio files may require longer processing times.
- API rate limits of Google Cloud Speech-To-Text and BigQuery apply.
- Custom vocabulary support is limited to the Speech-To-Text API.