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Continously ingest and deliver both streaming and batch change data from 100s of sources using Estuary's custom no-code connectors.
- <100ms Data pipelines
- 100+ Connectors
- 2-5x less than batch ELT
Amazon RDS for SQL Server connector details
Estuary’s Amazon RDS for SQL Server connector provides continuous Change Data Capture (CDC) from your SQL Server database into Flow collections, enabling real-time synchronization of inserts, updates, and deletes without batch processes. Built to leverage SQL Server’s native CDC capabilities, this connector ensures reliable and low-latency data replication for modern analytics and integration use cases.
- Streams change events in real time using SQL Server’s built-in CDC framework
- Automatically detects schema changes and manages new capture instances seamlessly
- Supports Automatic Capture Instance Management and Automatic Change Table Cleanup for minimal maintenance
- Handles backfills and incremental updates to ensure complete data consistency
- Offers enterprise-grade connectivity and security, including SSH tunneling, VPC access control, and support for BYOC or private deployments with Estuary
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 Amazon RDS for 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 Amazon RDS for SQL Server insights always reflect the latest data by connecting your databases to Amazon RDS for SQL Server with change data capture.
- Or connect critical SaaS apps to Amazon RDS for 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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