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

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Authenticate CloudTalk
Now, click the CloudTalk node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your CloudTalk settings. Authentication allows you to use CloudTalk through Latenode.
Configure the Google Cloud BigQuery and CloudTalk 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 CloudTalk 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 Google Cloud BigQuery, CloudTalk, 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 CloudTalk integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and CloudTalk (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 CloudTalk
CloudTalk + Google Sheets + Slack: When a new call ends in CloudTalk, the data is added as a new row in Google Sheets. If specific criteria are met (e.g., call duration exceeds a threshold), a message is sent to a designated Slack channel.
CloudTalk + Google BigQuery + Google Sheets: Each new call in CloudTalk triggers an automated data export to Google BigQuery. After exporting, the data will then be summarized, with key metrics calculated and displayed in Google Sheets.
Google Cloud BigQuery and CloudTalk 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 CloudTalk
Automate CloudTalk call and SMS data within Latenode. Trigger workflows on new calls, messages, or agent status changes. Update CRMs, send alerts, or generate reports automatically. Use Latenode's visual editor and data transformation tools to customize call center automations without complex coding, and scale your workflows efficiently.
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See how Latenode works
FAQ Google Cloud BigQuery and CloudTalk
How can I connect my Google Cloud BigQuery account to CloudTalk using Latenode?
To connect your Google Cloud BigQuery account to CloudTalk 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 CloudTalk accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze call data in BigQuery after calls in CloudTalk?
Yes, with Latenode you can automatically push CloudTalk call data to Google Cloud BigQuery. This provides advanced analytics and reporting, enhanced by Latenode's data transformation capabilities.
What types of tasks can I perform by integrating Google Cloud BigQuery with CloudTalk?
Integrating Google Cloud BigQuery with CloudTalk allows you to perform various tasks, including:
- Store call details from CloudTalk in Google Cloud BigQuery for analysis.
- Trigger CloudTalk actions based on data changes in Google Cloud BigQuery.
- Enrich Google Cloud BigQuery data with real-time information from CloudTalk.
- Create custom reports combining data from both Google Cloud BigQuery and CloudTalk.
- Automate data backups from CloudTalk to Google Cloud BigQuery.
What BigQuery data formats are supported within Latenode workflows?
Latenode supports standard BigQuery data types. You can transform and map data using visual tools or JavaScript for complex requirements.
Are there any limitations to the Google Cloud BigQuery and CloudTalk 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 might require JavaScript coding.
- Real-time updates depend on the API rate limits of both services.