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

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Authenticate Fibery
Now, click the Fibery node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Fibery settings. Authentication allows you to use Fibery through Latenode.
Configure the Google Cloud BigQuery and Fibery 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 Fibery 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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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Cloud BigQuery, Fibery, 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 Fibery integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Fibery (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 Fibery
Google Cloud BigQuery + Fibery + Slack: Monitors BigQuery for data updates, finds or creates corresponding entities in Fibery to reflect these changes, and then sends a message to a specified Slack channel to notify the team about the data modification.
Fibery + Google Cloud BigQuery + Google Sheets: Extracts data from Fibery, inserts it into Google Cloud BigQuery for analysis, and then summarizes key metrics in Google Sheets, creating a streamlined reporting workflow.
Google Cloud BigQuery and Fibery 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 Fibery
Sync Fibery's structured data—tasks, projects, wikis—into Latenode for automated workflows. Trigger actions like sending notifications on status changes or updating other tools. Latenode adds logic and integrations Fibery lacks, building complex flows with no code. Automate cross-functional workflows beyond Fibery's native capabilities.
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See how Latenode works
FAQ Google Cloud BigQuery and Fibery
How can I connect my Google Cloud BigQuery account to Fibery using Latenode?
To connect your Google Cloud BigQuery account to Fibery 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 Fibery accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I sync BigQuery data with Fibery?
Yes, you can! Latenode allows seamless data synchronization, so updates in Google Cloud BigQuery automatically reflect in Fibery. Keep your Fibery workspace current with critical data.
What types of tasks can I perform by integrating Google Cloud BigQuery with Fibery?
Integrating Google Cloud BigQuery with Fibery allows you to perform various tasks, including:
- Automate report generation in Fibery using BigQuery data.
- Visualize key performance indicators from BigQuery in Fibery dashboards.
- Trigger Fibery actions based on BigQuery data changes.
- Update Fibery entities with insights from BigQuery analysis.
- Create custom workflows to manage data pipelines across both platforms.
How secure is the Google Cloud BigQuery integration via Latenode?
Latenode utilizes secure authentication and encryption, ensuring that your Google Cloud BigQuery data is protected during integration and workflow execution.
Are there any limitations to the Google Cloud BigQuery and Fibery integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data mapping requires careful configuration.
- Complex data transformations might require JavaScript code.
- Large data volumes may impact workflow execution time.