Stream data from Amazon S3 to PostgreSQL
Move data from Amazon S3 to PostgreSQL in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Of connectors
- 5500+Active users
- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate Amazon S3 with PostgreSQL in 3 simple steps
Connect Amazon S3 as your data source
Set up a source connector for Amazon S3 in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
Configure PostgreSQL as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to PostgreSQL.
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.

Amazon S3 connector details
Estuary’s Amazon S3 connector captures and processes data from your S3 buckets or prefixes, automatically converting files into Estuary collections for downstream analytics and integration. It supports incremental ingestion of new or updated objects and automatically detects multiple data formats for flexible file-based pipelines.
- Continuously ingests data from S3 buckets or specific prefixes
- Supports CSV, JSON, Avro, and other formats with automatic schema detection
- Enables incremental syncs using lexicographic key ordering for efficiency
- Securely connects using AWS IAM roles or access keys
- Backed by Estuary’s private deployment and VPC security options for controlled access

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.
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.

Amazon S3 to PostgreSQL pricing estimate
Estimated monthly cost to move 800 GB from Amazon S3 to PostgreSQL is approximately $1,000.
Data moved
Choose how much data you want to move from Amazon S3 to PostgreSQL each month.
GB
Choose number of sources and destinations.
Why pay more?
Move the same data for a fraction of the cost.



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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this Amazon S3 to PostgreSQL integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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DataOps made simple
Add advanced capabilities like schema inference and evolution with a few clicks. Or automate your data pipeline and integrate into your existing DataOps using Estuary's rich CLI.





































