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

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Authenticate MongoDB
Now, click the MongoDB node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your MongoDB settings. Authentication allows you to use MongoDB through Latenode.
Configure the Google Cloud BigQuery (REST) and MongoDB 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 MongoDB 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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MongoDB
Trigger on Webhook
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery (REST), MongoDB, 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 MongoDB integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and MongoDB (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 MongoDB
Google Cloud BigQuery (REST) + MongoDB + Google Sheets: This automation queries data from Google Cloud BigQuery, inserts the results into a MongoDB collection, and then visualizes key metrics by adding rows to a Google Sheet.
MongoDB + Google Cloud BigQuery (REST) + Slack: This automation monitors MongoDB for document updates. When a critical change occurs, it triggers a query in Google Cloud BigQuery and sends a Slack notification with the query results.
Google Cloud BigQuery (REST) and MongoDB 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.
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About MongoDB
Use MongoDB in Latenode to automate data storage and retrieval. Aggregate data from multiple sources, then store it in MongoDB for analysis or reporting. Latenode lets you trigger workflows based on MongoDB changes, create real-time dashboards, and build custom integrations. Low-code tools and JavaScript nodes unlock flexibility for complex data tasks.
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See how Latenode works
FAQ Google Cloud BigQuery (REST) and MongoDB
How can I connect my Google Cloud BigQuery (REST) account to MongoDB using Latenode?
To connect your Google Cloud BigQuery (REST) account to MongoDB 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 MongoDB accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I synchronize BigQuery data with MongoDB collections?
Yes, you can! Latenode's visual editor simplifies data synchronization, allowing you to automate data transfers and transformations between Google Cloud BigQuery (REST) and MongoDB effortlessly.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with MongoDB?
Integrating Google Cloud BigQuery (REST) with MongoDB allows you to perform various tasks, including:
- Automating data warehousing from MongoDB into Google Cloud BigQuery (REST).
- Triggering MongoDB updates based on insights from Google Cloud BigQuery (REST) analysis.
- Creating real-time dashboards with combined data from both platforms.
- Enriching MongoDB data with aggregated Google Cloud BigQuery (REST) statistics.
- Building custom reporting pipelines for business intelligence purposes.
Can I use JavaScript to transform data between these services?
Yes! Latenode allows custom JavaScript code for advanced transformations, making data mapping between Google Cloud BigQuery (REST) and MongoDB highly flexible.
Are there any limitations to the Google Cloud BigQuery (REST) and MongoDB integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization may take time depending on dataset sizes.
- Complex data transformations might require advanced JavaScript knowledge.
- Rate limits of Google Cloud BigQuery (REST) and MongoDB still apply.