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

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

JobNimbus
Configure the JobNimbus
Click on the JobNimbus node to configure it. You can modify the JobNimbus 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 JobNimbus 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 JobNimbus 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 JobNimbus 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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Trigger on Webhook
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Save and Activate the Scenario
After configuring JobNimbus, 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 JobNimbus and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between JobNimbus 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 JobNimbus and Google Cloud BigQuery (REST)
JobNimbus + Google Cloud BigQuery (REST) + Google Sheets: When a job is updated in JobNimbus, the data is sent to BigQuery. BigQuery analyzes the data and updates a Google Sheet with a summary report.
Google Cloud BigQuery (REST) + JobNimbus + Slack: When a new row is added to a BigQuery table, indicating a project delay, the relevant project manager in JobNimbus is updated, and a notification is sent to a designated Slack channel.
JobNimbus and Google Cloud BigQuery (REST) integration alternatives
About JobNimbus
Automate Instagram Business tasks within Latenode. Schedule posts, extract comments, and analyze metrics without manual effort. Integrate Instagram data with CRMs or spreadsheets. Latenode offers flexible logic and affordable scaling, so you can manage social media workflows alongside other business processes using AI tools or custom scripts.
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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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FAQ JobNimbus and Google Cloud BigQuery (REST)
How can I connect my JobNimbus account to Google Cloud BigQuery (REST) using Latenode?
To connect your JobNimbus account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select JobNimbus and click on "Connect".
- Authenticate your JobNimbus and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze JobNimbus sales data in BigQuery?
Yes, you can! Latenode’s flexible data mapping lets you send JobNimbus data to BigQuery, enabling powerful sales analytics and reporting. Use AI for deeper insights.
What types of tasks can I perform by integrating JobNimbus with Google Cloud BigQuery (REST)?
Integrating JobNimbus with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Automatically back up JobNimbus data to a BigQuery dataset.
- Create custom sales dashboards using JobNimbus data in BigQuery.
- Analyze customer behavior trends from JobNimbus within BigQuery.
- Track marketing campaign performance using combined data sources.
- Generate reports on project profitability using JobNimbus and BigQuery.
What JobNimbus data can I access through Latenode?
Latenode allows access to contacts, jobs, estimates, invoices, and other key JobNimbus data for seamless integration and automation workflows.
Are there any limitations to the JobNimbus and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization may take time depending on the volume.
- Complex data transformations may require JavaScript knowledge.
- BigQuery costs are separate and depend on usage volume.