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- 2-5x less than batch ELT
Apache Iceberg connector details
The Apache Iceberg connector materializes Flow collections into Iceberg tables for large-scale analytics and lakehouse architectures. It orchestrates Spark jobs on AWS EMR Serverless (or another configured compute backend) to merge incoming changes, ensuring tables stay continuously up to date.
- Materializes Flow collections into Apache Iceberg tables for open table formats
- Orchestrates Spark jobs on AWS EMR Serverless to process updates
- Supports hard deletes for accurate CDC replication or soft deletes for cost savings
- Compatible with AWS Glue, S3 Tables, and Snowflake Open Catalogs via REST APIs
- Configurable sync schedules to balance performance and cost
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 to Apache Iceberg 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 Apache Iceberg insights always reflect the latest data by connecting your databases to Apache Iceberg with change data capture.
- Or connect critical SaaS apps to Apache Iceberg 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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