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

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

Google Cloud BigQuery
Configure the Google Cloud BigQuery
Click on the Google Cloud BigQuery node to configure it. You can modify the Google Cloud BigQuery 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 node, select MongoDB from the list of available apps, and choose the action you need from the list of nodes within MongoDB.

Google Cloud BigQuery
⚙

MongoDB

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 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 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.

JavaScript
⚙
AI Anthropic Claude 3
⚙

MongoDB
Trigger on Webhook
⚙
Google Cloud BigQuery
⚙
⚙
Iterator
⚙
Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery, 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 and MongoDB integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery 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 and MongoDB
Google Sheets + MongoDB + Google Sheets: When a new row is added to a Google Sheet, the data is used to update a document in MongoDB. The updated data is then written back to a different Google Sheet for reporting purposes.
MongoDB + BigQuery + Slack: When a document is updated in MongoDB, BigQuery is used to analyze the changes. If significant changes are detected, a notification is sent to a Slack channel to alert the team.
Google Cloud BigQuery and MongoDB integration alternatives
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

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.
Similar apps
Related categories
See how Latenode works
FAQ Google Cloud BigQuery and MongoDB
How can I connect my Google Cloud BigQuery account to MongoDB using Latenode?
To connect your Google Cloud BigQuery account to MongoDB on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery and click on "Connect".
- Authenticate your Google Cloud BigQuery and MongoDB accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I sync BigQuery data into MongoDB collections?
Yes, you can! Latenode simplifies data syncing with a visual interface and flexible data transformations, allowing seamless transfer of insights and real-time updates from Google Cloud BigQuery to MongoDB.
What types of tasks can I perform by integrating Google Cloud BigQuery with MongoDB?
Integrating Google Cloud BigQuery with MongoDB allows you to perform various tasks, including:
- Migrating historical Google Cloud BigQuery data to MongoDB for operational use.
- Triggering MongoDB updates based on insights from Google Cloud BigQuery analysis.
- Building real-time dashboards using MongoDB data enriched by BigQuery analytics.
- Creating automated reports based on combined Google Cloud BigQuery and MongoDB data.
- Synchronizing specific Google Cloud BigQuery datasets into MongoDB collections.
Whatdatatypesare supportedwhenmovingdatabetweenBigQueryandMongoDB?
Latenode supports most common data types, automatically converting between Google Cloud BigQuery and MongoDB formats where possible. Complex data structures may need custom transformation logic.
Are there any limitations to the Google Cloud BigQuery and MongoDB integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Very large datasets may require optimized workflows for efficient transfer.
- Complex data transformations might require custom JavaScript code.
- Real-time synchronization depends on the polling frequency and available API limits.