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Azure SQL Server connector details
The Microsoft SQL Server connector captures real-time change data (CDC) from your SQL Server database and streams it into Estuary collections, ensuring continuous synchronization across systems.
- Continuous CDC ingestion: Streams inserts, updates, and deletes directly from SQL Server’s CDC change tables into Flow collections for real-time data pipelines.
- Broad platform support: Works seamlessly across self-hosted, Azure SQL Database, Amazon RDS, and Google Cloud SQL instances.
- Automatic schema and instance handling: Detects new capture instances when DDL changes occur (like added columns) and automatically transitions to updated change tables.
- Granular permissions and automation: Supports automatic capture instance management and change table cleanup when granted
db_owner
privileges. - Flexible and secure connectivity: Connect via SSH tunneling or IP allowlisting for on-premise and cloud-hosted deployments.
- Customizable configuration: Includes advanced tuning options for backfill chunk size, table skipping, and metadata tagging.
💡 Tip: For optimal reliability, enable Automatic Capture Instance Management to let Flow handle table-level CDC setup and schema evolution automatically, while ensuring the user has the necessary db_owner
privileges.


HIGH THROUGHPUT
Distributed event-driven architecture enable boundless scaling with exactly-once semantics.

DURABLE REPLICATION
Cloud storage backed CDC w/ heart beats ensures reliability, even if your destination is down.

REAL-TIME INGESTION
Capture and relay every insert, update, and delete in milliseconds.
Real-timehigh throughput
Point a connector and replicate changes from Azure SQL Server in <100ms. Leverage high-availability, high-throughput Change Data Capture.Or choose from 100s of batch and real-time connectors to move and transform data using ELT and ETL.
- Ensure your Azure SQL Server insights always reflect the latest data by connecting your databases to Azure SQL Server with change data capture.
- Or connect critical SaaS apps to Azure SQL Server with real-time data pipelines.
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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 | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
Speed | <100ms | 5min+ | Varies | <100ms |
Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
Scale |

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