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Capture data from PostgreSQL Batch and deliver it to your destinations using Estuary's pre-built connectors. Build pipelines for real-time, incremental, or batch data movement based on connector capabilities.

<100ms to batch
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01. Move from PostgreSQL Batch02. Transform in-flight03. Select a destination

PostgreSQL Batch connector details

The PostgreSQL Batch Query connector in Estuary captures data from PostgreSQL databases by periodically executing queries and loading the results into Estuary collections. It is useful when logical replication is unavailable, when capturing from database views or certain read replicas, or when you need custom SQL queries.

XMIN-based incremental capture for regular PostgreSQL tables using the xmin system column
Initial full backfill followed by incremental updates when XMIN mode is used
Cursor-based incremental capture using fields such as timestamps or incrementing IDs
Full-refresh capture for tables or views without a cursor
Custom SQL queries for filtering, aggregations, or capturing specific subsets of data
Configurable polling schedules, with a default interval of 5 minutes
Optional discovery of PostgreSQL views
Automatic use of primary keys as Estuary collection keys when available
Support for self-hosted PostgreSQL and managed platforms, including Amazon RDS and Aurora, Google Cloud SQL, and Azure Database for PostgreSQL

For ordinary tables, discovered bindings use XMIN mode by default. The first poll performs a full backfill, and subsequent polls capture rows with newer transaction IDs. Other bindings can use cursor-based incremental queries, full refreshes, or custom query templates.

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

How to connect PostgreSQL Batch to your destination in 3 easy steps

1
Connect PostgreSQL Batch as your data source
Securely connect PostgreSQL Batch and choose the objects, tables, or collections you need to sync.
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
Deliver to your destination
Continuously or periodically deliver your data to the destination you choose, based on the capabilities and configuration of your pipeline.

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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. Capture data from PostgreSQL Batch using the cadence supported by the connector, then transform and deliver it to the destinations your team uses.

Connect PostgreSQL Batch to warehouses, databases, data lakes, search platforms, and event systems through Estuary-supported destinations.
Capture once and reuse PostgreSQL Batch data across transformations and multiple downstream destinations without rebuilding source extraction.

See where you can send PostgreSQL Batch data:

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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