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

Add the Google Cloud Pub\Sub Node
Select the Google Cloud Pub\Sub node from the app selection panel on the right.


Google Cloud Pub\Sub

Configure the Google Cloud Pub\Sub
Click on the Google Cloud Pub\Sub node to configure it. You can modify the Google Cloud Pub\Sub 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 Cloud Pub\Sub 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 Cloud Pub\Sub 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 Cloud Pub\Sub 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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Save and Activate the Scenario
After configuring Google Cloud Pub\Sub, 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 Cloud Pub\Sub and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Google Cloud Pub\Sub 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 Cloud Pub\Sub and Google Cloud BigQuery (REST)
Google Cloud Pub\Sub + Google Cloud BigQuery (REST) + Slack: When a new message is received in Google Cloud Pub/Sub, it triggers a query job in Google Cloud BigQuery to analyze the data. The results of the query are then sent as a message to a Slack channel.
Google Cloud BigQuery (REST) + Google Cloud Pub\Sub + Google Sheets: When a new row is added to a BigQuery table, a Pub/Sub message is published. This message triggers the addition of a row to a Google Sheet, logging the event.
Google Cloud Pub\Sub and Google Cloud BigQuery (REST) integration alternatives

About Google Cloud Pub\Sub
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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 Cloud Pub\Sub and Google Cloud BigQuery (REST)
How can I connect my Google Cloud Pub\Sub account to Google Cloud BigQuery (REST) using Latenode?
To connect your Google Cloud Pub\Sub account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud Pub\Sub and click on "Connect".
- Authenticate your Google Cloud Pub\Sub and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I archive Pub/Sub messages in BigQuery?
Yes, you can. Latenode simplifies this by automating data transfer, letting you archive messages without complex scripting. Benefit: improved data governance and compliance.
What types of tasks can I perform by integrating Google Cloud Pub\Sub with Google Cloud BigQuery (REST)?
Integrating Google Cloud Pub\Sub with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Stream real-time data from Pub/Sub topics into BigQuery datasets.
- Build data pipelines for analytics and business intelligence.
- Automate data warehousing and reporting processes.
- Trigger alerts based on data patterns identified in BigQuery.
- Enrich Pub/Sub data with data from other sources in BigQuery.
HowdoIhandlemessageformattingdifferencesbetweenPub/SubandBigQuery?
Latenode's data transformation blocks let you seamlessly map and reformat data, ensuring compatibility between Pub/Sub and BigQuery's schemas.
Are there any limitations to the Google Cloud Pub\Sub and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large data volumes may require optimization for efficient processing.
- Complex data transformations might necessitate JavaScript code blocks.
- Real-time dashboards depend on BigQuery's performance and cost.