How to connect Google Cloud BigQuery (REST) and Apify
Create a New Scenario to Connect Google Cloud BigQuery (REST) and Apify
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 (REST), triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery (REST) or Apify will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery (REST) or Apify, 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 Apify Node
Next, click the plus (+) icon on the Google Cloud BigQuery (REST) node, select Apify from the list of available apps, and choose the action you need from the list of nodes within Apify.

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Authenticate Apify
Now, click the Apify node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Apify settings. Authentication allows you to use Apify through Latenode.
Configure the Google Cloud BigQuery (REST) and Apify 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 Apify 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 BigQuery (REST), Apify, 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 (REST) and Apify integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Apify (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 Apify
Apify + Google Cloud BigQuery (REST) + Google Sheets: When an Apify actor run finishes, the data is fetched from the dataset and inserted into a BigQuery table. Then, a query is run in BigQuery to summarize key metrics, and the results are added as a new row to a Google Sheet for easy monitoring.
Google Cloud BigQuery (REST) + Apify + Slack: When a new row is added to a BigQuery table, Apify scrapes related web data using a specified actor. The scraped data is then summarized and posted to a designated Slack channel.
Google Cloud BigQuery (REST) and Apify 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 Apify
Use Apify in Latenode to extract web data at scale for lead generation, market research, and more. Apify handles complex scraping, while Latenode orchestrates the data: trigger workflows, transform results with AI, and send data to any app. Automate web actions visually and affordably.
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FAQ Google Cloud BigQuery (REST) and Apify
How can I connect my Google Cloud BigQuery (REST) account to Apify using Latenode?
To connect your Google Cloud BigQuery (REST) account to Apify 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 Apify accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze web scraping data in BigQuery?
Yes, you can! Latenode makes it easy to send Apify's scraped data to Google Cloud BigQuery (REST) for analysis, unlocking insights from web data with powerful querying.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Apify?
Integrating Google Cloud BigQuery (REST) with Apify allows you to perform various tasks, including:
- Store scraped website data directly into BigQuery datasets.
- Schedule regular data extractions and updates to BigQuery.
- Enrich BigQuery data with real-time web scraping results.
- Automate data analysis workflows using scraped information.
- Create dashboards visualizing scraped data stored in BigQuery.
HowdoIsecurelymanageBigQuerycredentialsinLatenode?
Latenodeusessecurecredentialstorageandencryptionto protectyourGoogleCloudBigQuery(REST)credentials,ensuringdata privacyandcompliance.
Are there any limitations to the Google Cloud BigQuery (REST) and Apify integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large data transfers may be subject to BigQuery API rate limits.
- Complex data transformations might require custom JavaScript code.
- Initial setup requires familiarity with BigQuery authentication.