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- 2-5x less than batch ELT
Amazon DynamoDB connector details
The Amazon DynamoDB materialization connector writes data from Estuary Flow collections into DynamoDB tables, enabling fast, scalable, and serverless data storage for real-time applications.
- High-performance integration: Continuously materializes Flow collections into DynamoDB tables for instant access to current data.
- Flexible configuration: Automatically maps collection keys to DynamoDB partition and sort keys, with support for custom projections.
- Collection constraints: Supports up to two collection keys per table, with a 400KB item size limit to maintain DynamoDB efficiency.
- Secure access: Uses AWS access key and secret access key credentials, following AWS IAM best practices for permission control.
- Custom endpoint support: Optionally connects to DynamoDB-compatible APIs via custom endpoint configuration.
- Simple setup: Requires only the AWS region, access credentials, and target table name to start materializing.
💡 Tip: If your Flow collection has more than two keys or large documents, use a derivation to create a smaller, optimized collection before materializing to DynamoDB.
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 Amazon DynamoDB 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 DynamoDB insights always reflect the latest data by connecting your databases to Amazon DynamoDB with change data capture.
- Or connect critical SaaS apps to Amazon DynamoDB 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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