Overview
Google Cloud SQL for SQL Server is a fully managed relational database service within Google Cloud. It handles automated backups, high availability with regional failover, patching, read replicas, and automatic storage scaling, so teams spend more time querying data and less time managing infrastructure. Cloud SQL for SQL Server is available in Enterprise and Enterprise Plus service editions and includes multiple SQL Server versions and editions, as outlined in the resources below.
Connecting Google Cloud SQL - SQL Server to Datagrid gives Datagrid's AI agents direct access to operational database objects. Datagrid imports databases, tables, views, stored procedures, functions, and users from your Cloud SQL instance on a configurable schedule. Once imported, that data becomes available inside Datagrid workflows, where agents can cross-check SQL Server records against CRM entries, generate narrative summaries from query results, and trigger downstream actions when specific data conditions appear.
The data flow is one-way from Cloud SQL for SQL Server into Datagrid. You select which objects to import, configure a sync schedule, and Datagrid keeps the dataset current. Cloud SQL for SQL Server also includes Change Data Capture (CDC) on SQL Server instances, which can complement time-sensitive workflows where applicable.
How to integrate Google Cloud SQL - SQL Server with Datagrid
This integration brings Cloud SQL database objects into Datagrid datasets so project teams can run agentic AI workflows on current operational data.
Connect your instance
Click + Create in the top left of the Datagrid screen.
Select Connect Apps.
Search for the Google Cloud SQL - SQL Server integration.
Enter your Cloud SQL instance details: server name, database name, username, and password.
Click Next.
Select the data objects you want to import: databases, tables, views, stored procedures, functions, or users.
Click Start First Import to begin syncing your dataset.
A typical setup uses the same fields listed in the Datagrid flow:
{
"server_name": "your-cloud-sql-server",
"database_name": "your-database",
"username": "your-username",
"password": "your-password",
"objects_to_import": [
"databases",
"tables",
"views",
"stored procedures",
"functions",
"users"
]
}
Configure sync settings
Open the left side panel and select your Google Cloud SQL - SQL Server dataset.
Click the ... menu in the top right corner of the dataset.
Click Edit Pipeline to rename or configure your integration.
Click the Schedule button next to Import Configuration.
Set the sync frequency: daily, weekly, or monthly.
Define any downtime windows when syncs should not run.
Click Update to save the new schedule.
You can represent those schedule choices in a simple configuration like this:
{
"frequency": "daily | weekly | monthly",
"preferred_time": "set in Datagrid",
"downtime_windows": "optional"
}
Authenticate access
The integration uses SQL Server built-in authentication, with a username and password that have appropriate privileges on your Cloud SQL instance. Your Cloud SQL instance must allow connections from Datagrid. For secure external connections, Google recommends using the Cloud SQL Auth Proxy or configuring SSL/TLS encryption.
Cloud SQL for SQL Server also includes Windows Authentication via Managed Active Directory and Microsoft Entra ID integration for organizations that require centralized identity management.
Review data sync details
Direction — One-way (Cloud SQL → Datagrid)
Objects synced — Databases, tables, views, stored procedures, functions, users
Frequency — Configurable: daily, weekly, or monthly
Authentication — SQL Server username and password
Once the integration is in place, Datagrid keeps the selected dataset current on the schedule you define.
Why use Google Cloud SQL - SQL Server with Datagrid
This integration fits teams that need answers and action from data that already lives in managed SQL Server environments.
Automated data extraction from managed SQL Server instances: Datagrid imports tables, views, and stored procedures from Cloud SQL on a recurring schedule, with no manual exports or SQL dump files required.
Agentic AI workflows that act on live operational data: Datagrid's AI agents cross-check imported SQL Server records with data from other connected platforms to flag inconsistencies, generate reports, and trigger actions.
Lower infrastructure overhead: Cloud SQL handles backups, patching, failover, and storage scaling. Datagrid handles the data pipeline, so teams can stay focused on decisions and exception handling.
Scheduled sync with downtime controls: Configure import frequency and define maintenance windows so data pulls do not conflict with peak database usage periods.
Full schema visibility across your organization: Import stored procedures, functions, and user data alongside tables and views, giving Datagrid's AI agents a broader picture of database structure and access patterns.
Part of a broader Google Cloud data stack: Combine with BigQuery or Google Cloud Storage to build workflows from operational data through analytics.
These benefits matter most when the database already holds the operational truth project teams need to cross-check, report on, and route.
What you can build with Google Cloud SQL - SQL Server Datagrid integration
Datagrid turns imported Cloud SQL data into workflow inputs for reporting, validation, and exception handling across connected systems.
Teams commonly use this integration in the following ways:
Cross-platform data validation for project teams: Import tables from Cloud SQL for SQL Server into Datagrid, then configure AI agents to compare records against data from your CRM or ERP integration. Agents flag mismatches, including duplicate customer records, outdated pricing data, and inconsistent inventory counts, then route exceptions to the responsible team member.
Automated business intelligence reporting: Schedule daily imports of sales, operations, or financial data from Cloud SQL. Datagrid's AI agents analyze trends across imported tables and generate narrative summaries, replacing the manual process of pulling data, building pivot tables, and writing status reports.
Database schema documentation and audit workflows: Import stored procedures, functions, and user access records into Datagrid. Datagrid's AI agents map relationships between database objects, identify unused stored procedures, and flag users with overly broad privileges. This creates a living audit trail that updates on every sync cycle and is useful for compliance reviews and security workflows.
Multi-source data enrichment for application development: Pull reference data from Cloud SQL for SQL Server into Datagrid alongside data from Google Cloud Storage or other database integrations like Cloud SQL for PostgreSQL. Datagrid's AI agents merge and transform datasets from multiple sources, producing enriched outputs ready for consumption by custom applications without writing transformation scripts.
These workflows are strongest when teams need one-way ingestion into Datagrid and coordinated action across multiple systems afterward.
Resources and documentation
Use the following references when you configure, secure, or extend the integration:
Datagrid Google Cloud SQL - SQL Server setup guide: prerequisites, configuration steps, and supported data objects
Google Cloud SQL for SQL Server introduction: product overview covering managed service capabilities and core concepts
Cloud SQL for SQL Server features page: supported and unsupported SQL Server features on Cloud SQL
Cloud SQL REST API reference: complete API endpoint documentation for Cloud SQL administration
Frequently asked questions
What SQL Server versions does Cloud SQL support?
Cloud SQL for SQL Server supports SQL Server 2017, 2019, 2022, and 2025. SQL Server 2017, 2019, and 2022 are available across Standard, Enterprise, Express, and Web editions, while SQL Server 2025 is available in Standard, Enterprise, and Express editions. The full list of supported versions and their mainstream support dates is available in the Cloud SQL supported database versions documentation.
How do I configure secure connections between Datagrid and my Cloud SQL instance?
You can configure SSL/TLS encryption for all connections to your Cloud SQL instance. For external connections, Google recommends the Cloud SQL Auth Proxy, which validates connections using IAM credentials and wraps them in an authorized SSL/TLS layer without requiring manual SSL certificate management or authorized network configuration.
What authentication methods are available for Cloud SQL for SQL Server?
Cloud SQL for SQL Server includes three authentication methods: built-in SQL Server authentication (username and password), Windows Authentication via Managed Active Directory or customer-managed AD, and Microsoft Entra ID integration. The Datagrid integration uses SQL Server authentication with instance credentials.
Can I use Change Data Capture (CDC) with this integration?
CDC is available on Cloud SQL for SQL Server instances. You enable it using a Cloud SQL-specific stored procedure (msdb.dbo.gcloudsql_cdc_enable_db) as documented in the CDC setup guide. CDC tracks row-level changes in your tables and can support source-side change tracking.
Are there known limitations when importing data from Cloud SQL for SQL Server?
Cloud SQL for SQL Server runs only one import or export operation at a time per instance, and backups are unavailable while import or export operations are in progress. The sysadmin role and CREATE ENDPOINT are not available on managed instances. Full details are in the Cloud SQL for SQL Server known issues documentation.
Similar integrations
Explore related Datagrid database and Google Cloud integrations:
Google Cloud SQL - PostgreSQL: Sibling Cloud SQL integration for PostgreSQL databases on Google Cloud, commonly used alongside SQL Server in multi-engine environments.
Google Cloud SQL - MySQL: The MySQL variant of Cloud SQL, completing the three-engine Cloud SQL family on Datagrid.
BigQuery: Google Cloud's serverless data warehouse and a common analytical destination for data extracted from Cloud SQL for SQL Server.