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

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

Google Analytics
Configure the Google Analytics
Click on the Google Analytics node to configure it. You can modify the Google Analytics URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Google Cloud BigQuery (REST) Node
Next, click the plus (+) icon on the Google Analytics 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 Google Analytics 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 Google Analytics 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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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Analytics, 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 Google Analytics and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Google Analytics 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 Google Analytics and Google Cloud BigQuery (REST)
Google Analytics + Google Cloud BigQuery (REST) + Google Sheets: Google Analytics runs a report on website traffic. The data is then inserted into a BigQuery table. Finally, Google Sheets retrieves the data from BigQuery and visualizes it.
Google Cloud BigQuery (REST) + Google Analytics + Slack: A new row is added to a BigQuery table (REST). This triggers a query job that analyzes Google Analytics data and, if a sudden traffic drop is detected, sends an alert to a Slack channel.
Google Analytics and Google Cloud BigQuery (REST) integration alternatives
About Google Analytics
Automate marketing insights using Google Analytics within Latenode. Track user behavior and trigger actions based on key metrics. Send data to CRMs, databases, or ad platforms automatically. Latenode streamlines analysis workflows without code, offering flexible logic and integrations, unlike manual reporting or limited point solutions.
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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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FAQ Google Analytics and Google Cloud BigQuery (REST)
How can I connect my Google Analytics account to Google Cloud BigQuery (REST) using Latenode?
To connect your Google Analytics account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Analytics and click on "Connect".
- Authenticate your Google Analytics and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate custom report generation and storage?
Yes, you can! Latenode enables automated report generation with custom metrics and dimensions. Store them directly in Google Cloud BigQuery (REST) using our visual interface and built-in scheduling.
What types of tasks can I perform by integrating Google Analytics with Google Cloud BigQuery (REST)?
Integrating Google Analytics with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Automating data exports for custom reporting and analysis.
- Combining web analytics with other business intelligence data.
- Building real-time dashboards with aggregated metrics.
- Triggering personalized marketing campaigns based on behavior.
- Analyzing user segments for targeted content delivery.
How do I handle large Google Analytics data volumes?
Latenode excels at scaling data workflows. Leverage serverless functions and batch processing to handle massive Google Analytics datasets seamlessly.
Are there any limitations to the Google Analytics and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Historical data backfills may require additional configuration.
- API rate limits from Google Analytics and Google Cloud BigQuery (REST) still apply.
- Complex custom dimensions might need JavaScript transformation.