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

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

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Monster API, 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 Monster API integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Monster API (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 Monster API
Monster API + Google Sheets: Fetch job postings from the Monster API and store the data, including job title, company, and location, in a Google Sheet for analysis and reporting.
Monster API + Slack: When a new job posting is found via the Monster API, send a notification to a specified Slack channel to alert recruiters of the new opportunity.
Google Cloud BigQuery and Monster API 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 Monster API
Struggling with unreliable or slow data? Integrate Monster API in Latenode to build automated data validation and cleansing workflows. Use its data enrichment and verification features to refine your data, then route the cleaned info to other services. Benefit from Latenode's visual editor and scalability for consistent, error-free data flow.
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See how Latenode works
FAQ Google Cloud BigQuery and Monster API
How can I connect my Google Cloud BigQuery account to Monster API using Latenode?
To connect your Google Cloud BigQuery account to Monster API 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 Monster API accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I enrich Monster API data using BigQuery analysis?
Yes, you can! Latenode enables seamless data enrichment, allowing you to enhance Monster API data with BigQuery's powerful analytical capabilities for better insights and decision-making.
What types of tasks can I perform by integrating Google Cloud BigQuery with Monster API?
Integrating Google Cloud BigQuery with Monster API allows you to perform various tasks, including:
- Analyzing recruitment trends using historical Monster API job posting data.
- Automating reports on candidate demographics stored in Google Cloud BigQuery.
- Building a real-time dashboard to visualize key recruitment metrics.
- Triggering personalized outreach based on Google Cloud BigQuery data analysis.
- Managing and updating candidate information across both platforms.
How does Latenode handle Google Cloud BigQuery data security?
Latenode employs robust security measures to protect your data, including encryption and secure authentication protocols, ensuring data integrity.
Are there any limitations to the Google Cloud BigQuery and Monster API integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization may take time depending on dataset sizes.
- Complex Google Cloud BigQuery queries might require optimization for performance.
- Rate limits on Monster API may affect the speed of data retrieval.