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Database ⇄ Data warehouse

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

Keep AWS Aurora PostgreSQL and Snowflake 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 Snowflake

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

Teams pair AWS Aurora PostgreSQL with Snowflake to keep an operational database and an analytics warehouse aligned. Aurora Tables and Rows feed Snowflake Schemas and Tables for reporting, while curated warehouse Views can flow back to power application features.

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

Common use cases

  • 01 Offload BI workloads from Aurora by querying replicated Tables in Snowflake instead of the production database.
  • 02 Keep Snowflake Databases and Schemas structurally in step with Aurora schema changes, including new Columns.
  • 03 Serve warehouse-computed scores from Snowflake Views inside the Aurora-backed product.
  • 04 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on

Common sync patterns

Operational analytics pipeline

Aurora Tables and Rows replicate into Snowflake Schemas and Tables so analysts query production data without hitting the primary database.

Change capture into Streams

row-level changes from Aurora land in Snowflake Tables tracked by Streams for incremental downstream processing.

Reverse ETL to the app

aggregates in Snowflake Materialized Views sync back into Aurora Tables that the application reads.

What you can sync between AWS Aurora PostgreSQL and Snowflake

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 Snowflake objects How this pairing syncs
Tables The core sync unit; rows are matched across systems by primary key. Tables The main landing and activation target for synced records. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. Schemas Namespaces within a database used to organize synced tables. Views and materialized views is specific to AWS Aurora PostgreSQL and Schemas to Snowflake — each maps to any object or custom field on the other side.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Views Modeled projections used as the source side of outbound syncs. Foreign keys is specific to AWS Aurora PostgreSQL and Views to Snowflake — each maps to any object or custom field on the other side.
Replication slots and publications The logical replication objects that power log-based CDC. Materialized Views Precomputed results synced outward for low-latency reads. Replication slots and publications is specific to AWS Aurora PostgreSQL and Materialized Views to Snowflake — each maps to any object or custom field on the other side.
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Streams Row-level change records on a table, consumed to process deltas instead of full scans. Databases and schemas is specific to AWS Aurora PostgreSQL and Streams to Snowflake — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. Stages File staging areas used for bulk loads into synced tables. Rows is specific to AWS Aurora PostgreSQL and Stages to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL and Snowflake

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

AWS Aurora PostgreSQL Snowflake Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

DeliveryEach detected change is applied to Snowflake as a row-level write, with types converted between the two schemas.

Snowflake AWS Aurora PostgreSQL Sub-second propagation

DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.

DeliveryEach detected change is applied to AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with AWS Aurora PostgreSQL ⇄ Snowflake

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

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

Real-time

Two-way sync

Changes in AWS Aurora PostgreSQL or Snowflake 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 Snowflake 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 Snowflake record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ Snowflake 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 Snowflake.

How the AWS Aurora PostgreSQL and Snowflake 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

Snowflake

Integration surface
SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
Authentication
Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
Change detection
Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
Capabilities
read · write · CDC
Rate limits
No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
Snowflake setup guide
How it works

How to connect AWS Aurora PostgreSQL to Snowflake — 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 Snowflake 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
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL and Snowflake 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 ⇄ Snowflake
    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 Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora PostgreSQL and Snowflake integration FAQ

SECURITY

Security teams trust 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
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 493 integrations available for AWS Aurora PostgreSQL and Snowflake.

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