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

Google Cloud BigQuery
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Nimble

Authenticate Nimble
Now, click the Nimble node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Nimble settings. Authentication allows you to use Nimble through Latenode.
Configure the Google Cloud BigQuery and Nimble 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 Nimble 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
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AI Anthropic Claude 3
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Nimble
Trigger on Webhook
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Google Cloud BigQuery
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Iterator
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Nimble, 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 Nimble integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Nimble (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 Nimble
Nimble + Google Sheets + Slack: When a new contact is added in Nimble, their details are added to a Google Sheet. A message is then sent to a Slack channel, notifying the team of the new contact and providing a link to the sheet.
Google Sheets + Nimble + Slack: When a new row is added to a Google Sheet, contact information is extracted to create a new contact in Nimble. A Slack message confirms the contact creation.
Google Cloud BigQuery and Nimble 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.
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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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See how Latenode works
FAQ Google Cloud BigQuery and Nimble
How can I connect my Google Cloud BigQuery account to Nimble using Latenode?
To connect your Google Cloud BigQuery account to Nimble 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 Nimble accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I update Nimble contacts with BigQuery data?
Yes, you can! Latenode's visual editor makes it simple to map BigQuery data fields to Nimble contact fields, ensuring your CRM is always up-to-date with the latest customer insights. Automate routine tasks!
What types of tasks can I perform by integrating Google Cloud BigQuery with Nimble?
Integrating Google Cloud BigQuery with Nimble allows you to perform various tasks, including:
- Automatically creating new Nimble contacts from BigQuery data analysis results.
- Enriching existing Nimble contact profiles with data from BigQuery queries.
- Triggering email campaigns in Nimble based on BigQuery data insights.
- Synchronizing contact activity from Nimble back to BigQuery for analysis.
- Generating custom reports in BigQuery using Nimble's contact data.
What BigQuery permissionsare requiredforLatenode toaccess my data?
Latenode requires read-only access to your BigQuery datasets to extract data. You control permissions, ensuring data security and privacy.
Are there any limitations to the Google Cloud BigQuery and Nimble 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 JavaScript for advanced customization.
- Real-time updates depend on the frequency of workflow execution.