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Deliver data from databases, SaaS apps, APIs, files, and streaming systems into PostgreSQL using Estuary's pre-built connectors. Run pipelines from real-time streaming to scheduled batch based on connector capabilities.

<100ms to batch
200+ connectors
Managed data pipelines

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01. Select a source02. Transform in-flight03. Deliver to PostgreSQL

PostgreSQL connector details

The PostgreSQL materialization connector writes Estuary collections into PostgreSQL tables and keeps them updated continuously as source data changes. Estuary creates and manages destination tables for each selected collection, applies ongoing inserts, updates, and deletes, and maintains schema consistency so PostgreSQL can serve real-time application, reporting, and operational workloads.

Materialize Estuary collections into PostgreSQL tables with low-latency, incremental updates.
Create and manage destination tables automatically based on your selected Estuary collections.
Apply inserts, updates, and deletes so PostgreSQL stays current with upstream source changes.
Connect using your PostgreSQL host, port, database name, schema, and database user credentials.
Use port 5432 by default when no custom port is specified.
Support self-hosted PostgreSQL, Amazon RDS, Amazon Aurora, Google Cloud SQL, Azure Database for PostgreSQL, and Supabase.
Use SSL, SSH tunneling, or cloud IAM authentication where supported for secure connectivity.
Handle schema evolution, table naming, and reserved words during materialization.

For more details about the PostgreSQL connector, check out the documentation page.

How to connect your data source to PostgreSQL in 3 easy steps

1
Connect your data source
Select from more than 100 supported databases and SaaS platforms including PostgreSQL, MySQL, SQL Server, MongoDB, and Kafka.
2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
3
Sync to PostgreSQL
Continuously or periodically deliver data into your destination with support for change data capture and reliable delivery for accurate insights.

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HIGH THROUGHPUT

Distributed, event-driven architecture scales for demanding data workloads.

DURABLE COLLECTIONS

Store data as it moves so you can transform, replay, and deliver it downstream.

FLEXIBLE LATENCY

From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.

From <100ms to batch

Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Bring data from databases, SaaS apps, APIs, files, and event streams into PostgreSQL, with capture and delivery cadence based on connector capabilities and pipeline configuration.

Connect data from operational databases, SaaS applications, APIs, files, and streaming systems to PostgreSQL through Estuary.
Transform and route data through Estuary collections before delivering it to PostgreSQL.

See what you can connect to PostgreSQL:

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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	$	$$-$$$$	$-$$$$	$-$$$$
Latency	<100ms to scheduled	5min+	Varies	<100ms
Ease	Analysts can manage	Analysts can manage	Data Engineer	Senior Data Engineer
Scale
Maintenance Effort	Low	Medium	High	High