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Database ⇄ Data warehouse

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

Keep Amazon Aurora 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 Amazon Aurora and Snowflake

Connect Amazon Aurora 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.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Amazon Aurora's rows in Snowflake, 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 Amazon Aurora where the services that read from it get them at normal query latency.

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

Common use cases

  • Feed finance reconciliation models from ERP data landed in Snowflake on a continuous basis
  • Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
  • Consolidate several Aurora clusters into one reporting database.
  • Write enriched or scored records from analytics pipelines back into the Aurora tables that power an application.

Offload heavy reads

Point analytical queries at the synced copy in Snowflake and keep Amazon Aurora focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon Aurora land in Snowflake as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

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

What you can sync between Amazon Aurora 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.

Amazon Aurora objects Snowflake objects
Views Read-only query-backed sources for downstream syncs. Views Modeled projections used as the source side of outbound syncs.
Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. Materialized Views Precomputed results synced outward for low-latency reads.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. Streams Row-level change records on a table, consumed to process deltas instead of full scans.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. Stages File staging areas used for bulk loads into synced tables.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Tasks Scheduled SQL used to transform synced data after it lands.
Databases Logical databases within a cluster that scope a sync connection. VARIANT Columns Semi-structured JSON payloads stored alongside relational columns.
What ships with Amazon Aurora ⇄ Snowflake

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

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

Real-time

Two-way sync

Changes in Amazon Aurora or Snowflake instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or Snowflake record.

Observability

Monitoring

Track your Amazon Aurora ⇄ Snowflake sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Aurora and Snowflake.

How the Amazon Aurora and Snowflake connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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 Amazon Aurora 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 Amazon Aurora 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
    Amazon Aurora connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Aurora 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 · Amazon Aurora ⇄ 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
    Amazon Aurora Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora and Snowflake 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 Amazon Aurora and Snowflake.

Popular · 8 of 386
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