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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
Oracle Database connector details
Estuary’s OracleDB connector captures change events from Oracle databases in real time using Oracle LogMiner. It continuously streams inserts, updates, and deletes into Flow collections, providing a reliable foundation for downstream analytics and CDC pipelines.
- Streams real-time CDC events from Oracle 11g and above
- Supports both standard and container databases (CDB/PDB) setups
- Enables automatic backfills, incremental SCN-based syncs, and dictionary management
- Compatible with secure SSH tunneling for private or cloud-hosted databases
- Supports Estuary’s Private and BYOC deployments for full control and compliance
💡 Tip: For managed Oracle databases on AWS or Azure, Estuary’s connector automatically configures LogMiner and watermark tracking, reducing setup time and ensuring high-throughput CDC performance.
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 Oracle Database 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 Oracle Database insights always reflect the latest data by connecting your databases to Oracle Database with change data capture.
- Or connect critical SaaS apps to Oracle Database with real-time data pipelines.
Don't see a connector?Request and our team will get back to you in 24 hours
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 |
Deliver real-time and batch data from DBs, SaaS, APIs, and more
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