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

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Data Enrichment
Authenticate Data Enrichment
Now, click the Data Enrichment node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Data Enrichment settings. Authentication allows you to use Data Enrichment through Latenode.
Configure the Google Cloud BigQuery (REST) and Data Enrichment 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 Data Enrichment 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 (REST), Data Enrichment, 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 Data Enrichment integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Data Enrichment (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 Data Enrichment
Google Cloud BigQuery (REST) + Data Enrichment + Google Sheets: Query data from Google Cloud BigQuery, enrich the data using Data Enrichment, and then insert the enriched data into a Google Sheet.
Salesforce + Data Enrichment + Google Cloud BigQuery (REST): When a new lead is created in Salesforce, enrich the lead's data. Then, insert the enriched lead data as a new row in a Google Cloud BigQuery table.
Google Cloud BigQuery (REST) and Data Enrichment 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 Data Enrichment
Enrich lead data, verify addresses, or flag fraud risks within Latenode workflows. Connect Data Enrichment APIs to auto-update records across apps. Streamline data cleaning and validation with no-code blocks or custom JS. Automate tasks that need enhanced data for better decisions, at scale.
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FAQ Google Cloud BigQuery (REST) and Data Enrichment
How can I connect my Google Cloud BigQuery (REST) account to Data Enrichment using Latenode?
To connect your Google Cloud BigQuery (REST) account to Data Enrichment 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 Data Enrichment accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I enrich BigQuery data using real-time data enrichment?
Yes, you can. Latenode's visual interface makes it easy to pull data from BigQuery, enrich it with Data Enrichment, and then write the updated data back. Improve your data quality effortlessly.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Data Enrichment?
Integrating Google Cloud BigQuery (REST) with Data Enrichment allows you to perform various tasks, including:
- Automatically enrich customer data stored in BigQuery with demographic details.
- Enhance marketing campaign data with real-time performance metrics.
- Cleanse and standardize address data in BigQuery for improved accuracy.
- Append missing contact information to leads stored in your BigQuery tables.
- Identify potential fraud by enriching transaction data with risk scores.
CanIautomateBigQuerydatatransformationusingLatenode’sintegration?
Yes! Latenode allows you to automate complex data transformations between BigQuery and Data Enrichment using its visual editor and code blocks.
Are there any limitations to the Google Cloud BigQuery (REST) and Data Enrichment integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Data Enrichment credits are separate and depend on your Data Enrichment subscription.
- Large datasets from Google Cloud BigQuery (REST) may require optimized workflows for performance.
- Custom Data Enrichment features might require JavaScript knowledge within Latenode.