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Database ⇄ Data warehouse

AWS Aurora PostgreSQL to AWS S3 integration — real-time, two-way sync

Keep AWS Aurora PostgreSQL and AWS S3 in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect AWS Aurora PostgreSQL and AWS S3

Connect AWS Aurora PostgreSQL and AWS S3 with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want AWS Aurora PostgreSQL's rows in AWS S3, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in AWS Aurora PostgreSQL where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in AWS Aurora PostgreSQL sync into AWS S3 in real time, and result tables in AWS S3 sync back into AWS Aurora PostgreSQL, with schema and type mapping between the two systems handled for you.

Common use cases

  • Stage bulk loads for warehouses that ingest from object storage.
  • Archive change history from ongoing syncs as timestamped files for audit and replay.
  • Capture row-level changes with logical replication and propagate them to SaaS tools without batch jobs.
  • Sync JSONB-heavy application data into structured objects in downstream business systems.

Offload heavy reads

Point analytical queries at the synced copy in AWS S3 and keep AWS Aurora PostgreSQL focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from AWS Aurora PostgreSQL land in AWS S3 as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in AWS S3 sync into AWS Aurora PostgreSQL, where whatever reads from that database gets them without querying the warehouse.

What you can sync between AWS Aurora PostgreSQL and AWS S3

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

AWS Aurora PostgreSQL objects AWS S3 objects
Replication slots and publications The logical replication objects that power log-based CDC. Object Metadata System and user-defined metadata read alongside object contents.
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Object Versions Prior copies retained when versioning is enabled, relevant for reprocessing.
Tables The core sync unit; rows are matched across systems by primary key. Event Notifications Notifications on object creation or deletion that trigger incremental processing.
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. Access Points Scoped network endpoints used to grant a sync narrow access to a bucket.
Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. Multipart Uploads The mechanism used to write large export files reliably.
Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. Buckets Top-level containers a sync targets; region and policy are set at this level.
What ships with AWS Aurora PostgreSQL ⇄ AWS S3

Connect AWS Aurora PostgreSQL and AWS S3 for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–AWS S3 connection.

Real-time

Two-way sync

Changes in AWS Aurora PostgreSQL or AWS S3 instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL or AWS S3 data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single AWS Aurora PostgreSQL or AWS S3 record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ AWS S3 sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and AWS S3.

How the AWS Aurora PostgreSQL and AWS S3 connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC

AWS S3

Integration surface
REST API (the S3 API), accessed directly or through AWS SDKs
Authentication
AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes
Change detection
S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge; list-based polling as a fallback
Capabilities
read · write · webhooks
Rate limits
Request throughput scales per prefix; sustained high-volume workloads should spread keys across prefixes
How it works

How to connect AWS Aurora PostgreSQL to AWS S3 — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate AWS Aurora PostgreSQL and AWS S3 with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    AWS Aurora PostgreSQL connected
    AWS S3 connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL and AWS S3 objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · AWS Aurora PostgreSQL ⇄ AWS S3
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    AWS Aurora PostgreSQL AWS S3
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora PostgreSQL and AWS S3 integration FAQ

SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for AWS Aurora PostgreSQL and AWS S3.

Popular · 8 of 386
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