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

Add the Google Cloud BigQuery (REST) Node
Select the Google Cloud BigQuery (REST) node from the app selection panel on the right.

Google Cloud BigQuery (REST)
Configure the Google Cloud BigQuery (REST)
Click on the Google Cloud BigQuery (REST) node to configure it. You can modify the Google Cloud BigQuery (REST) 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 BigQuery (REST) 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.

Google Cloud BigQuery (REST)
⚙
Google Cloud BigQuery
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 BigQuery (REST) 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 BigQuery (REST) 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.

JavaScript
⚙
AI Anthropic Claude 3
⚙
Google Cloud BigQuery
Trigger on Webhook
⚙
Google Cloud BigQuery (REST)
⚙
⚙
Iterator
⚙
Webhook response
Save and Activate the Scenario
After configuring Google Cloud BigQuery (REST), 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 BigQuery (REST) and Google Cloud BigQuery integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) 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 BigQuery (REST) and Google Cloud BigQuery
Google Cloud BigQuery (REST) + Google Cloud BigQuery + Google Sheets: Query BigQuery data using a REST API to get the data, then update a Google Sheet with the results, effectively creating an automated reporting system.
Google Cloud BigQuery (REST) + Google Cloud BigQuery + Slack: Use a REST API to query BigQuery, identify unusual data patterns, and then send a Slack notification to the team alerting them to the potential problem.
Google Cloud BigQuery (REST) and Google Cloud BigQuery integration alternatives
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.
Similar apps
Related categories
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.
Similar apps
Related categories
See how Latenode works
FAQ Google Cloud BigQuery (REST) and Google Cloud BigQuery
How can I connect my Google Cloud BigQuery (REST) account to Google Cloud BigQuery using Latenode?
To connect your Google Cloud BigQuery (REST) account to Google Cloud BigQuery on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery (REST) and click on "Connect".
- Authenticate your Google Cloud BigQuery (REST) and Google Cloud BigQuery accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate data warehousing from REST to BigQuery?
Yes, you can! Latenode enables seamless data warehousing automation. This helps keep data synchronized and provides real-time insights, leveraging Latenode’s low-code environment and scheduled tasks.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Google Cloud BigQuery?
Integrating Google Cloud BigQuery (REST) with Google Cloud BigQuery allows you to perform various tasks, including:
- Transfer data from REST API sources into BigQuery for analysis.
- Schedule automated data backups between different BigQuery projects.
- Create data pipelines to transform REST data before loading to BigQuery.
- Monitor REST API performance and log metrics into BigQuery.
- Trigger alerts based on data discrepancies between REST and BigQuery.
How do I handle errors when transferring data to BigQuery?
Latenode offers robust error handling, allowing you to log, retry, or route failed data transfers ensuring data integrity and workflow stability.
Are there any limitations to the Google Cloud BigQuery (REST) and Google Cloud BigQuery integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large datasets from REST APIs might require optimized data transformation steps.
- Complex REST API authentication schemes may require custom JavaScript code.
- Real-time synchronization may be limited by API request quotas.