How to connect Google Cloud BigQuery (REST) and Scrapeless
Create a New Scenario to Connect Google Cloud BigQuery (REST) and Scrapeless
In the workspace, click the βCreate New Scenarioβ button.

Add the First Step
Add the first node β a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Cloud BigQuery (REST), triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery (REST) or Scrapeless will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery (REST) or Scrapeless, and select the appropriate trigger to start the scenario.

Add the Google Cloud BigQuery (REST) Node
Select the Google Cloud BigQuery (REST) node from the app selection panel on the right.

Google Cloud BigQuery (REST)
Configure the Google Cloud BigQuery (REST)
Click on the Google Cloud BigQuery (REST) node to configure it. You can modify the Google Cloud BigQuery (REST) URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Scrapeless Node
Next, click the plus (+) icon on the Google Cloud BigQuery (REST) node, select Scrapeless from the list of available apps, and choose the action you need from the list of nodes within Scrapeless.

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Authenticate Scrapeless
Now, click the Scrapeless node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Scrapeless settings. Authentication allows you to use Scrapeless through Latenode.
Configure the Google Cloud BigQuery (REST) and Scrapeless 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 (REST) and Scrapeless 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 donβt 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 BigQuery (REST), Scrapeless, and any additional nodes, donβt 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 βRun onceβ and triggering an event to check if the Google Cloud BigQuery (REST) and Scrapeless integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Scrapeless (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 (REST) and Scrapeless
Scrapeless + Google Cloud BigQuery (REST) + Google Sheets: This workflow scrapes data from a website using Scrapeless, then inserts this data into a Google Cloud BigQuery table. Finally, it fetches insights from BigQuery using a query and saves the results into a Google Sheet for reporting.
Scrapeless + Google Cloud BigQuery (REST) + Slack: This workflow scrapes product prices from a website using Scrapeless. The scraped data is then analyzed using Google Cloud BigQuery, and if a price change is detected based on some query logic, an alert is sent to a designated Slack channel.
Google Cloud BigQuery (REST) and Scrapeless integration alternatives
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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About Scrapeless
Use Scrapeless in Latenode to extract structured data from websites without code. Scrape product details, news, or social media feeds, then pipe the data into your Latenode workflows. Automate lead generation, price monitoring, and content aggregation. Combine Scrapeless with Latenode's AI nodes for smarter data processing.
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See how Latenode works
FAQ Google Cloud BigQuery (REST) and Scrapeless
How can I connect my Google Cloud BigQuery (REST) account to Scrapeless using Latenode?
To connect your Google Cloud BigQuery (REST) account to Scrapeless on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery (REST) and click on "Connect".
- Authenticate your Google Cloud BigQuery (REST) and Scrapeless accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze scraped product data in BigQuery?
Yes, you can! Latenode enables this with a visual interface and custom JavaScript. Scrape data with Scrapeless, send it to Google Cloud BigQuery (REST), and gain powerful insights.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Scrapeless?
Integrating Google Cloud BigQuery (REST) with Scrapeless allows you to perform various tasks, including:
- Automatically archive scraped web data into a BigQuery dataset.
- Enrich existing BigQuery data with real-time scraped web content.
- Schedule regular scrapes and store the results in BigQuery.
- Trigger scraping based on data changes detected in BigQuery.
- Create reports combining scraped data and BigQuery analytics.
How do I handle large datasets in Google Cloud BigQuery (REST) on Latenode?
Latenode's serverless architecture scales automatically. Use batch processing and efficient queries to manage large scraped datasets easily within Google Cloud BigQuery (REST).
Are there any limitations to the Google Cloud BigQuery (REST) and Scrapeless integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex Scrapeless configurations may require advanced JavaScript knowledge.
- BigQuery costs depend on your data storage and query usage.
- Rate limits on both Scrapeless and Google Cloud BigQuery (REST) APIs apply.