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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 Aurora for MySQL connector details
Optimized for cloud-native databases, the Estuary MySQL connector for Amazon Aurora streams real-time change data using Change Data Capture (CDC) via the binary log. Once configured with the proper Aurora parameter group and permissions, it captures inserts, updates, and deletes from your Aurora MySQL cluster directly into Flow collections. With support for secure SSH tunneling, read replicas, and automatic backfill, it ensures reliable, low-latency synchronization from Aurora to downstream systems.
- Continuous CDC streaming from Amazon Aurora MySQL
- Requires binlog_format=ROW and 7-day retention for reliability
- Compatible with Aurora read replicas
- Supports secure access via SSH tunneling or IP allowlisting
- Simplifies real-time ingestion to any connected destination
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 Aurora for MySQL 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 Aurora for MySQL insights always reflect the latest data by connecting your databases to Amazon Aurora for MySQL with change data capture.
- Or connect critical SaaS apps to Amazon Aurora for MySQL 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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