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

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Authenticate LearnDash
Now, click the LearnDash node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your LearnDash settings. Authentication allows you to use LearnDash through Latenode.
Configure the Google Cloud BigQuery and LearnDash 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 LearnDash 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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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Cloud BigQuery, LearnDash, 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 LearnDash integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and LearnDash (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 LearnDash
LearnDash + Google Sheets + Slack: When a user completes a course in LearnDash, their completion data is added to a Google Sheet. Slack then sends a notification to a designated channel to celebrate or follow up.
LearnDash + Google Sheets + Slack: When a user enrolls in a course in LearnDash, their information is added to a Google Sheet. If the user enrolls in a specific course, a notification is sent to a Slack channel.
Google Cloud BigQuery and LearnDash 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 LearnDash
Automate LearnDash course management in Latenode. Enroll users automatically, send progress updates, and trigger actions in other apps based on course completion. Keep student data in sync with your CRM and marketing tools. Build custom learning paths visually; scale processes without code or per-step fees.
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See how Latenode works
FAQ Google Cloud BigQuery and LearnDash
How can I connect my Google Cloud BigQuery account to LearnDash using Latenode?
To connect your Google Cloud BigQuery account to LearnDash 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 LearnDash accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate LearnDash enrollment based on BigQuery data?
Yes, you can! Latenode allows seamless automation, triggering LearnDash enrollments directly from Google Cloud BigQuery data changes. This ensures timely course access for relevant users, boosting engagement.
What types of tasks can I perform by integrating Google Cloud BigQuery with LearnDash?
Integrating Google Cloud BigQuery with LearnDash allows you to perform various tasks, including:
- Automatically enrolling users in courses based on BigQuery data analysis.
- Updating user profiles in LearnDash with data from Google Cloud BigQuery.
- Generating custom reports combining course data and BigQuery analytics.
- Triggering personalized learning paths based on BigQuery insights.
- Analyzing user engagement metrics stored in BigQuery to improve course design.
What BigQuery data formats are supported in Latenode?
Latenode supports standard BigQuery data types, including integers, strings, dates, and arrays, for flexible data handling.
Are there any limitations to the Google Cloud BigQuery and LearnDash integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex data transformations might require JavaScript code.
- Very large BigQuery datasets could impact workflow execution speed.
- Real-time data synchronization is subject to API rate limits.