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

<100ms to batch
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01. Select a source02. Transform in-flight03. Deliver to Google Cloud SQL for PostgreSQL

Google Cloud SQL for PostgreSQL connector details

The Google Cloud SQL for PostgreSQL materialization connector in Estuary delivers data from Estuary collections into PostgreSQL tables hosted on Google Cloud SQL. It supports continuous materialization from real-time or batch pipelines, using standard merge-based updates by default with optional delta updates for applicable workloads.

Standard merge-based updates by default to keep destination rows synchronized
Optional delta updates for workloads where changes should be applied incrementally
Optional hard deletes so records deleted upstream can also be removed from destination tables
Automatic table creation for configured materialization bindings
Configurable PostgreSQL schemas and table names for individual bindings
Google Cloud IAM or username/password authentication
Direct Google Cloud SQL connectivity or SSH tunneling for private network configurations
Configurable SSL connection behavior
Custom SQL during table creation for indexes or other table-level configuration
Automatic quoting of PostgreSQL reserved words when used as field names

Estuary creates the destination tables used by the materialization; manually pre-created tables are not supported. The default PostgreSQL schema is public, but you can configure another default schema or override it for individual bindings.

For more details about the Google Cloud SQL for PostgreSQL connector, check out the documentation page.

How to connect your data source to Google Cloud SQL for 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 Google Cloud SQL for 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 Google Cloud SQL for 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 Google Cloud SQL for PostgreSQL through Estuary.
Transform and route data through Estuary collections before delivering it to Google Cloud SQL for 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