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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



Timescale connector details
The TimescaleDB materialization connector writes data from Estuary collections into TimescaleDB tables, making it easy to manage and query real-time, time-series workloads. Built on the PostgreSQL connector, it provides the same configuration experience with added support for Hypertables—TimescaleDB’s time-series optimized storage.
- PostgreSQL-compatible: Fully supports PostgreSQL features and syntax while extending for time-series optimization
- Hypertable support: Easily create Hypertables using custom SQL statements in the binding configuration
- Automatic table creation: Flow creates and manages tables within your TimescaleDB database automatically
- Flexible configuration: Supports both standard and delta updates for efficient data synchronization
- Ideal for time-series data: Perfect for IoT telemetry, metrics, financial data, and operational analytics
💡 Tip: Enable Hypertables by adding SELECT create_hypertable(‘table’, ‘timestamp_column’); in your binding configuration to unlock TimescaleDB’s performance benefits for time-series queries.


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