How to connect Amazon S3 and Google Cloud BigQuery (REST)
Create a New Scenario to Connect Amazon S3 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 Amazon S3, triggered by another scenario, or executed manually (for testing purposes). In most cases, Amazon S3 or Google Cloud BigQuery (REST) will be your first step. To do this, click "Choose an app," find Amazon S3 or Google Cloud BigQuery (REST), and select the appropriate trigger to start the scenario.

Add the Amazon S3 Node
Select the Amazon S3 node from the app selection panel on the right.


Amazon S3

Configure the Amazon S3
Click on the Amazon S3 node to configure it. You can modify the Amazon S3 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 Amazon S3 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 Amazon S3 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 Amazon S3 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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AI Anthropic Claude 3
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Trigger on Webhook
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Save and Activate the Scenario
After configuring Amazon S3, 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 Amazon S3 and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Amazon S3 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 Amazon S3 and Google Cloud BigQuery (REST)
Amazon S3 + Google Sheets: When a new file is uploaded to an Amazon S3 bucket, its metadata (name, size, etc.) is added as a new row in a Google Sheet for easy analysis and tracking.
Google Sheets + Amazon S3: When a new row is added to a Google Sheet, this row's data is then used to create and upload a new file into an Amazon S3 bucket.
Amazon S3 and Google Cloud BigQuery (REST) integration alternatives

About Amazon S3
Automate S3 file management within Latenode. Trigger flows on new uploads, automatically process stored data, and archive old files. Integrate S3 with your database, AI models, or other apps. Latenode simplifies complex S3 workflows with visual tools and code options for custom logic.
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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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FAQ Amazon S3 and Google Cloud BigQuery (REST)
How can I connect my Amazon S3 account to Google Cloud BigQuery (REST) using Latenode?
To connect your Amazon S3 account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Amazon S3 and click on "Connect".
- Authenticate your Amazon S3 and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate data transfer from S3 to BigQuery?
Yes, you can! Latenode's visual editor simplifies data transfers, letting you automate processes that would normally require complex scripting. Schedule regular transfers, or trigger them based on S3 events.
What types of tasks can I perform by integrating Amazon S3 with Google Cloud BigQuery (REST)?
Integrating Amazon S3 with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Load data from Amazon S3 into Google Cloud BigQuery for analysis.
- Trigger data transformations in BigQuery when new files are added to S3.
- Archive processed data from BigQuery back to Amazon S3 for long-term storage.
- Create data pipelines for ETL (Extract, Transform, Load) processes.
- Monitor Amazon S3 buckets and update BigQuery tables based on file changes.
How does Latenode handle large file transfers from Amazon S3?
Latenode supports efficient large file handling by leveraging streaming and parallel processing, ensuring minimal memory usage and faster transfer times.
Are there any limitations to the Amazon S3 and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial setup requires valid AWS and Google Cloud credentials.
- Data transfer speeds are subject to network bandwidth limitations.
- Complex data transformations may require JavaScript coding within Latenode.