How to connect Google Cloud BigQuery (REST) and Harvest
Create a New Scenario to Connect Google Cloud BigQuery (REST) and Harvest
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 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.

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 Harvest Node
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.

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Authenticate 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.
Configure the Google Cloud BigQuery (REST) and Harvest 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 Harvest 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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Harvest
Trigger on Webhook
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Webhook response

Save and Activate the Scenario
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.
Test the Scenario
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.
Most powerful ways to connect Google Cloud BigQuery (REST) and Harvest
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.
Google Cloud BigQuery (REST) and Harvest 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 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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FAQ Google Cloud BigQuery (REST) and Harvest
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:
- 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 Harvest accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
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:
- Automatically backing up Harvest time entry data to BigQuery.
- Creating custom reports combining time data and project budgets.
- Analyzing team efficiency trends stored in Harvest using BigQuery.
- Triggering alerts based on time tracking anomalies in BigQuery.
- Visualizing Harvest data using BigQuery's data visualization tools.
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:
- Historical data migration from Harvest to BigQuery may require custom scripting for large datasets.
- Complex data transformations might necessitate JavaScript coding within Latenode.
- Real-time synchronization depends on the API rate limits of both Google Cloud BigQuery (REST) and Harvest.