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

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

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

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Authenticate Google Cloud BigQuery (REST)
Now, click the Google Cloud BigQuery (REST) 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 (REST) settings. Authentication allows you to use Google Cloud BigQuery (REST) through Latenode.
Configure the Google AI and Google Cloud BigQuery (REST) 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 AI and Google Cloud BigQuery (REST) 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 AI, Google Cloud BigQuery (REST), 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 AI and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Google AI and Google Cloud BigQuery (REST) (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google AI and Google Cloud BigQuery (REST)
Google Sheets + Google AI + Google Sheets: When a new row is added to a Google Sheet, use Gemini to generate insights based on the data in that row. Then, add these insights as a comment to the same row in the Google Sheet.
Google Sheets + Slack + Google AI: When a new row is added to Google Sheets, send a message to a specified Slack channel. Then, use Gemini to generate an image based on the data in the new row, and send it as another message to the Slack channel.
Google AI and Google Cloud BigQuery (REST) integration alternatives
About Google AI
Use Google AI in Latenode to add smarts to your workflows. Process text, translate languages, or analyze images automatically. Unlike direct API calls, Latenode lets you combine AI with other apps, add logic, and scale without code. Automate content moderation, sentiment analysis, and more.
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About Google Cloud BigQuery (REST)
Automate BigQuery data workflows in Latenode. Query and analyze massive datasets directly within your automation scenarios, bypassing manual SQL. Schedule queries, transform results with JavaScript, and pipe data to other apps. Scale your data processing without complex coding or expensive per-operation fees. Perfect for reporting, analytics, and data warehousing automation.
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See how Latenode works
FAQ Google AI and Google Cloud BigQuery (REST)
How can I connect my Google AI account to Google Cloud BigQuery (REST) using Latenode?
To connect your Google AI account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google AI and click on "Connect".
- Authenticate your Google AI and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze AI-generated content using BigQuery?
Yes, you can! Latenode lets you pipe AI outputs into BigQuery for analysis. Get insights, track performance, and refine your AI models, all with a powerful no-code workflow.
What types of tasks can I perform by integrating Google AI with Google Cloud BigQuery (REST)?
Integrating Google AI with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Analyze sentiment of AI-generated text and store results in BigQuery.
- Use AI to categorize data in BigQuery for improved data organization.
- Create AI-powered dashboards with data sourced and refined from BigQuery.
- Automate the process of training AI models using BigQuery datasets.
- Enrich BigQuery data with AI-generated insights and predictions.
How does Latenode handle authentication for Google AI and BigQuery?
Latenode uses secure OAuth to authenticate Google AI and Google Cloud BigQuery (REST), ensuring your credentials remain private and protected.
Are there any limitations to the Google AI and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large datasets may require optimized BigQuery queries for efficient processing.
- Complex AI models might require significant computational resources.
- Real-time data processing depends on Google AI and BigQuery API rate limits.