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

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Authenticate Streamtime
Now, click the Streamtime node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Streamtime settings. Authentication allows you to use Streamtime through Latenode.
Configure the Google Cloud BigQuery and Streamtime 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 Streamtime 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, Streamtime, 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 Streamtime integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Streamtime (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 Streamtime
Streamtime + Google Cloud BigQuery + Google Sheets: When a job is completed in Streamtime, its data is sent to Google Cloud BigQuery. BigQuery analysis results are then used to update key metrics in a Google Sheets spreadsheet for visualization.
Streamtime + Google Cloud BigQuery + Slack: When a job is updated in Streamtime, the updated data is sent to Google Cloud BigQuery. If BigQuery identifies that a project exceeds the budget, a notification is sent to a Slack channel.
Google Cloud BigQuery and Streamtime 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 Streamtime
Streamtime project management inside Latenode: automate tasks like invoice creation based on project status, or sync time entries with accounting. Connect Streamtime to other apps via Latenode's visual editor and AI tools. Customize further with JavaScript for complex workflows. Manage projects and data automatically.
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See how Latenode works
FAQ Google Cloud BigQuery and Streamtime
How can I connect my Google Cloud BigQuery account to Streamtime using Latenode?
To connect your Google Cloud BigQuery account to Streamtime 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 Streamtime accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate project costing analysis using Google Cloud BigQuery and Streamtime?
Yes, you can. Latenode enables automated workflows for streamlined project cost analysis between Google Cloud BigQuery and Streamtime, boosting efficiency and providing data-driven insights.
What types of tasks can I perform by integrating Google Cloud BigQuery with Streamtime?
Integrating Google Cloud BigQuery with Streamtime allows you to perform various tasks, including:
- Automatically updating Streamtime with analyzed financial data from BigQuery.
- Creating reports on project profitability using data from both platforms.
- Syncing client data between BigQuery data warehouses and Streamtime.
- Triggering alerts in Streamtime based on BigQuery data analysis results.
- Generating custom invoices based on BigQuery insights and Streamtime projects.
Can I use JavaScript to transform data between Google Cloud BigQuery and Streamtime?
Yes, Latenode's JavaScript code blocks allow advanced data transformations, providing flexibility beyond standard data mapping when using Google Cloud BigQuery.
Are there any limitations to the Google Cloud BigQuery and Streamtime integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization might require manual configuration for complex datasets.
- Real-time data transfer depends on the API limits of both Google Cloud BigQuery and Streamtime.
- Custom queries in Google Cloud BigQuery need to be optimized for efficient data retrieval.