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

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Authenticate Microsoft SQL Server
Now, click the Microsoft SQL Server node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Microsoft SQL Server settings. Authentication allows you to use Microsoft SQL Server through Latenode.
Configure the Google Cloud BigQuery (REST) and Microsoft SQL Server 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 Microsoft SQL Server 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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Microsoft SQL Server
Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Cloud BigQuery (REST), Microsoft SQL Server, 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 Microsoft SQL Server integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Microsoft SQL Server (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 Microsoft SQL Server
Google Cloud BigQuery (REST) + Microsoft SQL Server + Google Sheets: This automation retrieves data from Google Cloud BigQuery and Microsoft SQL Server via queries. It then combines the datasets and visualizes the results in a Google Sheet for reporting purposes.
Microsoft SQL Server + Google Cloud BigQuery (REST) + Slack: This automation monitors Microsoft SQL Server data for anomalies by comparing it against Google Cloud BigQuery benchmarks. When a significant deviation is detected via a custom query, a notification is sent to a designated Slack channel.
Google Cloud BigQuery (REST) and Microsoft SQL Server 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 Microsoft SQL Server
Use Microsoft SQL Server in Latenode to automate database tasks. Directly query, update, or insert data in response to triggers. Sync SQL data with other apps; simplify data pipelines for reporting and analytics. Build automated workflows without complex coding to manage databases efficiently and scale operations.
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FAQ Google Cloud BigQuery (REST) and Microsoft SQL Server
How can I connect my Google Cloud BigQuery (REST) account to Microsoft SQL Server using Latenode?
To connect your Google Cloud BigQuery (REST) account to Microsoft SQL Server 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 Microsoft SQL Server accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I sync BigQuery data with SQL Server for reporting?
Yes, you can! Latenode allows scheduled data synchronization between Google Cloud BigQuery (REST) and Microsoft SQL Server, enabling real-time reporting and analysis without manual data transfers.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Microsoft SQL Server?
Integrating Google Cloud BigQuery (REST) with Microsoft SQL Server allows you to perform various tasks, including:
- Automating data warehousing processes between systems.
- Creating data backups from BigQuery to SQL Server.
- Synchronizing customer data for unified views.
- Enriching SQL Server data with BigQuery analytics.
- Triggering SQL Server updates from BigQuery insights.
Can Latenode transform data between BigQuery and SQL Server?
Yes, Latenode offers robust transformation options via code blocks and prompt-based AI to ensure seamless data compatibility between apps.
Are there any limitations to the Google Cloud BigQuery (REST) and Microsoft SQL Server integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large data transfers may be subject to API rate limits.
- Complex data transformations may require custom JavaScript code.
- Real-time synchronization depends on the polling interval set.