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

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

Save and Activate the Scenario
After configuring Google Cloud BigQuery, PostgreSQL, 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 PostgreSQL integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and PostgreSQL (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 PostgreSQL
Google Sheets + PostgreSQL + Slack: When a new row is added to a Google Sheet, the data is inserted into a PostgreSQL database, and a notification is sent to a Slack channel.
PostgreSQL + Google Sheets + Slack: When a new or updated row is detected in PostgreSQL, the relevant data is retrieved and used to update a specific row in Google Sheets, then a Slack message is sent to notify a channel of the update.
Google Cloud BigQuery and PostgreSQL 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 PostgreSQL
Use PostgreSQL in Latenode to automate database tasks. Build flows that react to database changes or use stored data to trigger actions in other apps. Automate reporting, data backups, or sync data across systems without code. Scale complex data workflows easily within Latenode's visual editor.
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See how Latenode works
FAQ Google Cloud BigQuery and PostgreSQL
How can I connect my Google Cloud BigQuery account to PostgreSQL using Latenode?
To connect your Google Cloud BigQuery account to PostgreSQL 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 PostgreSQL accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I sync BigQuery data with PostgreSQL for real-time dashboards?
Yes, you can! Latenode's visual interface simplifies data synchronization, letting you update PostgreSQL with BigQuery data automatically. Build live dashboards with ease.
What types of tasks can I perform by integrating Google Cloud BigQuery with PostgreSQL?
Integrating Google Cloud BigQuery with PostgreSQL allows you to perform various tasks, including:
- Automating data warehousing from application databases to BigQuery.
- Creating real-time inventory dashboards using combined datasets.
- Enriching PostgreSQL data with large-scale analytics from BigQuery.
- Triggering PostgreSQL updates based on BigQuery ML predictions.
- Building custom reporting workflows combining structured and unstructured data.
How does Latenode handle data type conversions between the two databases?
Latenode provides built-in data transformation blocks, JavaScript support, and AI-powered suggestions to ensure seamless data type mapping during integration.
Are there any limitations to the Google Cloud BigQuery and PostgreSQL integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization of large datasets can take time.
- Complex data transformations may require custom JavaScript code.
- API rate limits of Google Cloud BigQuery and PostgreSQL still apply.