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

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


Nimble

Add the Google Cloud BigQuery (REST) Node
Next, click the plus (+) icon on the Nimble 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 Nimble 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 Nimble 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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Webhook response

Save and Activate the Scenario
After configuring Nimble, 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 Nimble and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Nimble 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 Nimble and Google Cloud BigQuery (REST)
Nimble + Google Cloud BigQuery (REST) + Google Sheets: When a new contact is created in Nimble, the contact data is inserted into a BigQuery table. BigQuery then analyzes the CRM data, and the results are used to update a Google Sheet for sales performance reporting.
Google Cloud BigQuery (REST) + Nimble + Slack: BigQuery monitors customer engagement metrics derived from Nimble data. When BigQuery detects a significant drop in engagement, it triggers a Slack message to alert the sales team.
Nimble and Google Cloud BigQuery (REST) integration alternatives

About Nimble
Use Nimble within Latenode to enrich contact data and automate outreach. Update your CRM, personalize emails, and trigger follow-ups based on engagement—all visually. Latenode handles the workflow logic and scale, while Nimble provides targeted contact intelligence for smarter automation.
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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 Nimble and Google Cloud BigQuery (REST)
How can I connect my Nimble account to Google Cloud BigQuery (REST) using Latenode?
To connect your Nimble account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Nimble and click on "Connect".
- Authenticate your Nimble and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze Nimble contacts' deals data in BigQuery?
Yes, you can! Latenode's data transformation features let you easily move and prepare Nimble data for analysis in BigQuery, uncovering valuable sales insights with scalable workflows.
What types of tasks can I perform by integrating Nimble with Google Cloud BigQuery (REST)?
Integrating Nimble with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Backing up Nimble contact data to a secure BigQuery dataset.
- Creating custom reports on sales performance using BigQuery's analytics.
- Enriching Nimble contacts with demographic data stored in BigQuery.
- Triggering automated alerts based on sales data thresholds in BigQuery.
- Segmenting Nimble contacts based on BigQuery data for targeted campaigns.
Can I use JavaScript code to enhance data transformations?
Yes! Latenode allows you to integrate JavaScript code for advanced data manipulation and custom logic between Nimble and BigQuery.
Are there any limitations to the Nimble and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data loading from Nimble to BigQuery may take time for large datasets.
- The complexity of data transformations can impact workflow execution speed.
- API rate limits of Nimble and BigQuery may affect high-volume data transfers.