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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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