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Aggregate Harvest time tracking data into Google Cloud BigQuery (REST) for advanced reporting. Latenode’s visual editor simplifies the data pipeline, while affordable execution-based pricing scales with your business needs.
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In the workspace, click the “Create New Scenario” button.

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 Harvest will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery (REST) or Harvest, and select the appropriate trigger to start the scenario.

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

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.
Next, click the plus (+) icon on the Google Cloud BigQuery (REST) node, select Harvest from the list of available apps, and choose the action you need from the list of nodes within Harvest.

Google Cloud BigQuery (REST)
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Harvest
Now, click the Harvest node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Harvest settings. Authentication allows you to use Harvest through Latenode.
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Use various Latenode nodes to transform data and enhance your integration:

JavaScript
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AI Anthropic Claude 3
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Harvest
Trigger on Webhook
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Google Cloud BigQuery (REST)
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Iterator
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Webhook response
After configuring Google Cloud BigQuery (REST), Harvest, 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.
Run the scenario by clicking “Run once” and triggering an event to check if the Google Cloud BigQuery (REST) and Harvest integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Harvest (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Harvest + Google Cloud BigQuery (REST) + Google Sheets: When a new time entry is created in Harvest, the data is inserted into a BigQuery table for analysis. A query job analyzes the data and updates a Google Sheet with summary reports.
Harvest + Google Cloud BigQuery (REST) + Slack: When a new time entry is created in Harvest, the data is inserted into a BigQuery table. BigQuery analyzes the time entries and, if project budgets are exceeded, sends a Slack message to the project manager.
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 Harvest
Automate time tracking with Harvest in Latenode. Sync time entries to accounting, payroll, or project management. Create flows that auto-generate invoices or trigger alerts for budget overruns. Latenode provides the flexibility to connect Harvest data to other apps and add custom logic, avoiding manual updates and delays.
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How can I connect my Google Cloud BigQuery (REST) account to Harvest using Latenode?
To connect your Google Cloud BigQuery (REST) account to Harvest on Latenode, follow these steps:
Can I analyze Harvest time entries using BigQuery?
Yes, you can! Latenode’s no-code interface enables automated data transfers to BigQuery, giving you advanced analytics without complex scripting or data pipelines.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Harvest?
Integrating Google Cloud BigQuery (REST) with Harvest allows you to perform various tasks, including:
HowsecureistheGoogleCloudBigQuery(REST)integrationonLatenode?
Latenode uses secure OAuth authentication and encrypts sensitive data in transit and at rest, ensuring secure data transfer and processing.
Are there any limitations to the Google Cloud BigQuery (REST) and Harvest integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of: