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- <100ms Data pipelines
- 100+ Connectors
- 2-5x less than batch ELT



Sentry connector details
The Sentry capture connector streams observability and error-tracking data from your Sentry projects directly into Estuary Flow collections, enabling real-time monitoring and analytics alongside other event data sources.
- Supported resources: Events, Issues, Projects, and Releases
- Authentication: Uses a Sentry Auth Token along with your organization and project slugs
- Automatic discovery: Each Sentry resource is automatically mapped to a dedicated Flow collection
- Flexible setup: Configure directly in the Flow web app or by editing your Flow specification file
- Optimized performance: Enhanced from the open-source version for speed and reliability within Flow
💡 Tip: Use this connector to centralize error events from Sentry and combine them with application logs or customer data for unified observability pipelines.


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 Sentry 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 Sentry insights always reflect the latest data by connecting your databases to Sentry with change data capture.
- Or connect critical SaaS apps to Sentry 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 |

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