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

Add the Microsoft SQL Server Node
Select the Microsoft SQL Server node from the app selection panel on the right.


Microsoft SQL Server

Configure the Microsoft SQL Server
Click on the Microsoft SQL Server node to configure it. You can modify the Microsoft SQL Server URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Google Cloud BigQuery Node
Next, click the plus (+) icon on the Microsoft SQL Server node, select Google Cloud BigQuery from the list of available apps, and choose the action you need from the list of nodes within Google Cloud BigQuery.


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Authenticate Google Cloud BigQuery
Now, click the Google Cloud BigQuery 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 settings. Authentication allows you to use Google Cloud BigQuery through Latenode.
Configure the Microsoft SQL Server and Google Cloud BigQuery 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 Microsoft SQL Server and Google Cloud BigQuery 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 Microsoft SQL Server, Google Cloud BigQuery, 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 Microsoft SQL Server and Google Cloud BigQuery integration works as expected. Depending on your setup, data should flow between Microsoft SQL Server and Google Cloud BigQuery (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Microsoft SQL Server and Google Cloud BigQuery
Microsoft SQL Server + Google Cloud BigQuery + Google Sheets: Whenever a new or updated row is detected in Microsoft SQL Server, the data is replicated to Google Cloud BigQuery. Then, a query is executed in BigQuery and the results are presented in Google Sheets as a new row.
Google Cloud BigQuery + Microsoft SQL Server + Google Sheets: Execute a query in Google Cloud BigQuery, store the results in Microsoft SQL Server by inserting rows, and then present a summary of the inserted data in Google Sheets.
Microsoft SQL Server and Google Cloud BigQuery integration alternatives

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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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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See how Latenode works
FAQ Microsoft SQL Server and Google Cloud BigQuery
How can I connect my Microsoft SQL Server account to Google Cloud BigQuery using Latenode?
To connect your Microsoft SQL Server account to Google Cloud BigQuery on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Microsoft SQL Server and click on "Connect".
- Authenticate your Microsoft SQL Server and Google Cloud BigQuery accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate data warehousing from SQL Server to BigQuery?
Yes, you can easily automate data warehousing using Latenode. Schedule routine data transfers, apply transformations with JavaScript blocks, and ensure data integrity. Benefit from efficient and reliable data management.
What types of tasks can I perform by integrating Microsoft SQL Server with Google Cloud BigQuery?
Integrating Microsoft SQL Server with Google Cloud BigQuery allows you to perform various tasks, including:
- Migrating data from on-premise SQL Server databases to Google Cloud BigQuery.
- Creating real-time dashboards using aggregated SQL Server data in BigQuery.
- Enriching BigQuery datasets with transactional data from Microsoft SQL Server.
- Automating data backups from SQL Server to a BigQuery data warehouse.
- Building machine learning models using combined datasets from both platforms.
How does Latenode handle SQL Server data type conversions?
Latenode provides built-in data type conversion tools and JavaScript blocks for handling any compatibility issues, ensuring smooth data transfer and integration.
Are there any limitations to the Microsoft SQL Server and Google Cloud BigQuery integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial data synchronization may take time depending on database size.
- Complex SQL Server stored procedures might require adjustments for BigQuery.
- Real-time data replication depends on network latency and SQL Server resources.