How to connect Knack and Google Cloud BigQuery (REST)
Create a New Scenario to Connect Knack and Google Cloud BigQuery (REST)
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 Knack, triggered by another scenario, or executed manually (for testing purposes). In most cases, Knack or Google Cloud BigQuery (REST) will be your first step. To do this, click "Choose an app," find Knack or Google Cloud BigQuery (REST), and select the appropriate trigger to start the scenario.

Add the Knack Node
Select the Knack node from the app selection panel on the right.


Knack

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


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

Save and Activate the Scenario
After configuring Knack, Google Cloud BigQuery (REST), 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 Knack and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Knack and Google Cloud BigQuery (REST) (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Knack and Google Cloud BigQuery (REST)
Knack + Google Cloud BigQuery (REST) + Google Sheets: When a new record is created in Knack, the data is inserted into Google Cloud BigQuery for analysis. The analysis results are then used to update a Google Sheets spreadsheet for visualization and reporting.
Google Cloud BigQuery (REST) + Knack + Slack: When a new row is added to a BigQuery table, the flow searches for a matching record in Knack. If found, Knack record is updated, then a Slack message is sent to notify a channel about the BigQuery event and Knack update.
Knack and Google Cloud BigQuery (REST) integration alternatives

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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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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See how Latenode works
FAQ Knack and Google Cloud BigQuery (REST)
How can I connect my Knack account to Google Cloud BigQuery (REST) using Latenode?
To connect your Knack account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Knack and click on "Connect".
- Authenticate your Knack and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I archive Knack data in BigQuery?
Yes, you can! Latenode lets you automate data backups to BigQuery. This ensures data durability and enables advanced analysis with BigQuery's powerful tools.
What types of tasks can I perform by integrating Knack with Google Cloud BigQuery (REST)?
Integrating Knack with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Sync Knack records to BigQuery for data warehousing.
- Analyze Knack data using BigQuery's SQL interface.
- Create custom reports based on Knack and other data sources.
- Automate data transformations between Knack and BigQuery.
- Trigger Knack actions based on BigQuery data changes.
How secure is my Knack data when using Latenode?
Latenode employs robust security measures, including encryption and secure authentication, to protect your data during integration and transit.
Are there any limitations to the Knack and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data loading from Knack to BigQuery may take time.
- Complex data transformations might require custom JavaScript.
- BigQuery costs depend on your query complexity and data volume.