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

Google Cloud BigQuery
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Knack

Authenticate Knack
Now, click the Knack node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Knack settings. Authentication allows you to use Knack through Latenode.
Configure the Google Cloud BigQuery and Knack 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 Knack 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.

JavaScript
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AI Anthropic Claude 3
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Knack
Trigger on Webhook
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Google Cloud BigQuery
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Iterator
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Knack, 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 Knack integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Knack (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 Knack
Google Cloud BigQuery + Knack + Google Sheets: Analyze data in BigQuery, find corresponding records in Knack based on the analysis, and update a Google Sheet with the findings and updated Knack record information.
Knack + Google Cloud BigQuery + Slack: When a new record is created in Knack, the information is added to Google Cloud BigQuery, and a Slack message is sent to a specified channel, notifying the team about the new record.
Google Cloud BigQuery and Knack 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 Knack
Use Knack with Latenode to build custom database apps and automate workflows. Connect Knack data to other services, like CRMs or marketing tools, without code. Latenode lets you transform and route Knack data, create advanced logic, and scale automation beyond Knack's built-in limits, all visually and affordably.
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FAQ Google Cloud BigQuery and Knack
How can I connect my Google Cloud BigQuery account to Knack using Latenode?
To connect your Google Cloud BigQuery account to Knack 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 Knack accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I synchronize BigQuery data with a Knack database?
Yes, you can! Latenode’s visual editor simplifies synchronizing Google Cloud BigQuery data, updating Knack records automatically. This ensures data consistency and reduces manual updates.
What types of tasks can I perform by integrating Google Cloud BigQuery with Knack?
Integrating Google Cloud BigQuery with Knack allows you to perform various tasks, including:
- Automatically updating Knack records based on BigQuery analysis results.
- Creating visualizations in Knack using data extracted from BigQuery.
- Triggering alerts in Knack when BigQuery data meets specific conditions.
- Populating BigQuery tables with new data submitted through Knack forms.
- Scheduling regular data exports from BigQuery to update Knack databases.
Can I use JavaScript with Google Cloud BigQuery in Latenode?
Yes! Latenode allows JavaScript code within your workflows to perform custom data transformations or manipulate data before writing to Google Cloud BigQuery.
Are there any limitations to the Google Cloud BigQuery and Knack integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex data transformations may require custom JavaScript coding.
- The number of concurrent API requests is subject to rate limits.
- Large datasets might require optimization for optimal performance.