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

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Authenticate HighLevel
Now, click the HighLevel node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your HighLevel settings. Authentication allows you to use HighLevel through Latenode.
Configure the Google Cloud BigQuery and HighLevel 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 HighLevel 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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Save and Activate the Scenario
After configuring Google Cloud BigQuery, HighLevel, 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 HighLevel integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and HighLevel (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 HighLevel
HighLevel + Google Cloud BigQuery + Google Sheets: Analyze HighLevel marketing campaign performance data stored (or loaded) in Google Cloud BigQuery. Use Google Sheets to create a dashboard and visualize key performance indicators (KPIs) fetched from BigQuery.
HighLevel + Google Cloud BigQuery + Slack: When a HighLevel opportunity is updated, this data is reflected in BigQuery. The workflow then checks BigQuery for high-value lead patterns. When a pattern is found, a notification is sent to the sales team on Slack.
Google Cloud BigQuery and HighLevel 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 HighLevel
Use HighLevel with Latenode to automate marketing and sales tasks. Sync leads, trigger follow-ups, and manage campaigns within visual workflows. Connect HighLevel to other apps, enrich data with AI, and scale operations without complex coding. Latenode offers flexible automation at a predictable cost.
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See how Latenode works
FAQ Google Cloud BigQuery and HighLevel
How can I connect my Google Cloud BigQuery account to HighLevel using Latenode?
To connect your Google Cloud BigQuery account to HighLevel 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 HighLevel accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate lead segmentation using BigQuery data in HighLevel?
Yes, you can! Latenode's data transformation features allow you to process BigQuery data and automatically update HighLevel contacts, ensuring targeted campaigns.
What types of tasks can I perform by integrating Google Cloud BigQuery with HighLevel?
Integrating Google Cloud BigQuery with HighLevel allows you to perform various tasks, including:
- Automatically updating HighLevel contacts based on BigQuery analysis.
- Creating custom reports in HighLevel using data from Google Cloud BigQuery.
- Triggering HighLevel campaigns based on BigQuery data thresholds.
- Analyzing marketing ROI by combining ad spend and lead conversion data.
- Segmenting HighLevel leads using advanced data models from BigQuery.
How do I handle large datasets from BigQuery within Latenode automations?
Latenode's efficient data streaming handles massive BigQuery datasets, enabling quick transformations before updating HighLevel without performance bottlenecks.
Are there any limitations to the Google Cloud BigQuery and HighLevel integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data schema setup requires familiarity with BigQuery.
- Complex data transformations may require JavaScript for advanced manipulation.
- HighHighLevel API rate limits might affect large data syncs.