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

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

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Pipefy, 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 Pipefy integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Pipefy (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 Pipefy
Pipefy + Google Sheets + Slack: When a card is created in Pipefy, its details are added to a Google Sheet for tracking. A Slack notification is then sent to a designated channel, informing the team about the new card and providing a link to the relevant sheet.
Pipefy + Google Sheets + Slack: When a card is updated in Pipefy (e.g., moved to a new phase), the corresponding row in a Google Sheet is updated with the new status. Subsequently, a Slack message is sent to a specific channel to notify the team about the card's progress.
Google Cloud BigQuery and Pipefy 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 Pipefy
Orchestrate Pipefy process management inside Latenode for end-to-end automation. Trigger flows on card changes, create new cards from external data, and sync Pipefy data with other apps. Bypass manual updates and build complex workflows using visual tools, code, or AI — without step limits or vendor lock-in.
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FAQ Google Cloud BigQuery and Pipefy
How can I connect my Google Cloud BigQuery account to Pipefy using Latenode?
To connect your Google Cloud BigQuery account to Pipefy 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 Pipefy accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze Pipefy data in Google Cloud BigQuery?
Yes, you can. Latenode simplifies this, automatically syncing Pipefy data to Google Cloud BigQuery for in-depth analytics, improving decision-making with comprehensive insights.
What types of tasks can I perform by integrating Google Cloud BigQuery with Pipefy?
Integrating Google Cloud BigQuery with Pipefy allows you to perform various tasks, including:
- Automatically backing up Pipefy data to Google Cloud BigQuery for data security.
- Creating custom reports blending Pipefy data with other business metrics.
- Triggering Pipefy actions based on insights from Google Cloud BigQuery analysis.
- Performing sentiment analysis on Pipefy feedback using Google Cloud data.
- Generating personalized Pipefy task recommendations from Google Cloud BigQuery.
HowsecureistheGoogleCloudBigQueryconnectioninLatenode?
Latenode uses secure authentication and encryption to protect your Google Cloud BigQuery connection, ensuring data privacy and compliance.
Are there any limitations to the Google Cloud BigQuery and Pipefy integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data sync can take time depending on the data volume.
- Complex queries in Google Cloud BigQuery might require some SQL knowledge.
- Real-time data synchronization depends on API rate limits of both services.