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

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

Firecrawl
Configure the Firecrawl
Click on the Firecrawl node to configure it. You can modify the Firecrawl 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 Firecrawl 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 Firecrawl 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 Firecrawl 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 Firecrawl, 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 Firecrawl and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Firecrawl 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 Firecrawl and Google Cloud BigQuery (REST)
Firecrawl + Google Cloud BigQuery (REST) + Slack: Firecrawl crawls a specified URL. The extracted data is then inserted as a new row into a BigQuery table. Finally, a Slack message is sent to a designated channel to alert the marketing team about the website update.
Google Cloud BigQuery (REST) + Firecrawl + Google Sheets: A new row in BigQuery triggers a Firecrawl action to crawl a specific URL. Once the crawl is complete, the extracted data is then added as a new row to a Google Sheet.
Firecrawl and Google Cloud BigQuery (REST) integration alternatives
About Firecrawl
Use Firecrawl in Latenode to extract structured data from websites at scale. Monitor product prices, track competitors, or collect research data automatically. Unlike standalone scrapers, Latenode lets you integrate scraped data into complex workflows with custom logic and direct API connections, all without code.
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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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See how Latenode works
FAQ Firecrawl and Google Cloud BigQuery (REST)
How can I connect my Firecrawl account to Google Cloud BigQuery (REST) using Latenode?
To connect your Firecrawl account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Firecrawl and click on "Connect".
- Authenticate your Firecrawl and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze crawled website data in BigQuery?
Yes, you can. Latenode simplifies sending Firecrawl data to BigQuery for analysis. Extract insights with automated workflows. Leverage Latenode's data transformation tools for compatibility.
What types of tasks can I perform by integrating Firecrawl with Google Cloud BigQuery (REST)?
Integrating Firecrawl with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Automatically storing website crawl results directly into BigQuery datasets.
- Creating dashboards to visualize crawled website SEO performance metrics.
- Triggering alerts based on crawled content changes using BigQuery's analysis.
- Performing historical analysis of website content trends within BigQuery.
- Enriching crawled data with other data sources stored in Google Cloud.
HowdoIhandlescalingFirecrawlworkloadsonLatenode?
Latenode offers built-in scaling. Run parallel Firecrawl instances and process large datasets efficiently using Latenode's serverless architecture and compute resources.
Are there any limitations to the Firecrawl and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data schema setup in BigQuery may require manual configuration.
- Very large Firecrawl result sets may require optimized batch processing.
- Real-time data updates depend on Firecrawl's crawling frequency.