Move your data from Google Cloud SQL for SQL Server with your free account

Capture data from Google Cloud SQL for SQL Server and deliver it to your destinations using Estuary's pre-built connectors. Build pipelines for real-time, incremental, or batch data movement based on connector capabilities.

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01. Move from Google Cloud SQL for SQL Server02. Transform in-flight03. Select a destination

Google Cloud SQL for SQL Server connector details

The Google Cloud SQL for SQL Server source connector captures inserts, updates, and deletes from Cloud SQL for SQL Server databases using SQL Server Change Data Capture. After an initial backfill of selected tables, Estuary reads from SQL Server CDC change tables and writes the data into Estuary collections so downstream systems stay current in near real time.

Capture change events from Google Cloud SQL for SQL Server databases that support SQL Server CDC.
Enable CDC on both the source database and each table you want to capture before running the connector.
Use tables with primary keys when possible; for tables without primary keys, manually define the Estuary collection key during capture setup.
Connect using the Cloud SQL instance public IP or an SSH tunnel, with SQL Server traffic on port 1433.
Grant the capture user SELECT access on the source schemas and cdc schema, access to CDC change tables, and VIEW DATABASE STATE or VIEW DATABASE PERFORMANCE STATE.
Optionally use Automatic Capture Instance Management or Automatic Change Table Cleanup, but these features require db_owner permissions.

For more details about the Google Cloud SQL for SQL Server connector, check out the documentation page.

How to connect Google Cloud SQL for SQL Server to your destination in 3 easy steps

1
Connect Google Cloud SQL for SQL Server as your data source
Securely connect Google Cloud SQL for SQL Server and choose the objects, tables, or collections you need to sync.
2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
3
Deliver to your destination
Continuously or periodically deliver your data to the destination you choose, based on the capabilities and configuration of your pipeline.

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HIGH THROUGHPUT

Distributed, event-driven architecture scales for demanding data workloads.

DURABLE COLLECTIONS

Store data as it moves so you can transform, replay, and deliver it downstream.

FLEXIBLE LATENCY

From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.

From <100ms to batch

Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Capture data from Google Cloud SQL for SQL Server using the cadence supported by the connector, then transform and deliver it to the destinations your team uses.

Connect Google Cloud SQL for SQL Server to warehouses, databases, data lakes, search platforms, and event systems through Estuary-supported destinations.
Capture once and reuse Google Cloud SQL for SQL Server data across transformations and multiple downstream destinations without rebuilding source extraction.

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Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.

Feature Comparison

	Estuary	Batch ELT/ETL	DIY Python	Kafka
Price	$	$$-$$$$	$-$$$$	$-$$$$
Latency	<100ms to scheduled	5min+	Varies	<100ms
Ease	Analysts can manage	Analysts can manage	Data Engineer	Senior Data Engineer
Scale
Maintenance Effort	Low	Medium	High	High