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Database ⇄ Data warehouse

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

Keep AWS Aurora MySQL 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.

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

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Why teams connect AWS Aurora MySQL and Snowflake

Connect AWS Aurora MySQL 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 connect AWS Aurora MySQL to Snowflake to keep operational data available for analytics without querying the production database. Aurora MySQL Tables and Rows land in Snowflake Schemas and Tables, where Views, Materialized Views, and Streams support downstream reporting and transformation.

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

Common use cases

  • 01 Offload analytical queries from the Aurora MySQL production instance to Snowflake.
  • 02 Build historical reporting on Snowflake Tables while Aurora MySQL keeps only current operational Rows.
  • 03 Preserve Primary keys and indexes from Aurora MySQL as the merge keys for deduplicated Snowflake Tables.
  • 04 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on

Common sync patterns

Operational replication to the warehouse

Aurora MySQL Tables and Rows sync continuously into Snowflake Tables within the target Database and Schema.

Change capture for transformations

inserts and updates to Aurora MySQL Rows feed Snowflake Streams so downstream models process only changed records.

Reporting layer maintenance

Aurora MySQL Views are mirrored as Snowflake Views and Materialized Views for BI consumption.

What you can sync between AWS Aurora MySQL 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 MySQL objects Snowflake objects How this pairing syncs
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. 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 Can serve as read-only sync sources for derived or filtered datasets. Views Modeled projections used as the source side of outbound syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. Materialized Views Precomputed results synced outward for low-latency reads. Foreign keys is specific to AWS Aurora MySQL and Materialized Views to Snowflake — each maps to any object or custom field on the other side.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. Streams Row-level change records on a table, consumed to process deltas instead of full scans. Stored procedures and triggers is specific to AWS Aurora MySQL and Streams to Snowflake — each maps to any object or custom field on the other side.
Databases (schemas) Logical namespaces that scope which tables a sync connection can see. Stages File staging areas used for bulk loads into synced tables. Databases (schemas) is specific to AWS Aurora MySQL and Stages to Snowflake — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. Tasks Scheduled SQL used to transform synced data after it lands. Rows is specific to AWS Aurora MySQL and Tasks to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL 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 MySQL Snowflake Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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 MySQL 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 MySQL 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 MySQL ⇄ Snowflake

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL 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 MySQL 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 MySQL or Snowflake record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ 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 MySQL and Snowflake.

How the AWS Aurora MySQL and Snowflake connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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 MySQL 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 MySQL 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 MySQL connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

AWS Aurora MySQL 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 491 integrations available for AWS Aurora MySQL and Snowflake.

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