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Database ⇄ CRM

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

Keep AWS Aurora MySQL and Shopify 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 MySQL and Shopify

Treat Shopify like part of your database: its records live in AWS Aurora MySQL as real tables, and writes in either place sync to the other in seconds.

Teams sync Shopify with AWS Aurora MySQL to get store data — Products, Orders, and Customers — into relational Tables and Rows for analytics, fulfillment, and catalog management. Database-side writes flow back, so the catalog can be managed from systems that speak SQL.

Stacksync mirrors Orders, Customers, Abandoned Checkouts, Products from Shopify into Stored procedures and triggers, Databases (schemas), Tables, Rows in AWS Aurora MySQL with real-time, bi-directional sync. Read CRM records with plain queries; write updates from your application and they appear in Shopify with validation intact. Go-to-market teams keep working in the CRM, engineers keep working in the database, and neither has to think about the other.

Common use cases

  • 01 Join Shopify Customers with warehouse and support data in Aurora for a unified customer view.
  • 02 Bulk-update ProductVariants pricing via SQL instead of manual admin edits or CSV imports.
  • 03 Analyze ProductMedias and catalog completeness with database queries across the full product set.
  • 04 Sync orders, customers, and inventory into Postgres for operational reporting across stores.

Common sync patterns

Order data pipeline

Shopify Orders and Customers sync into Aurora Tables for fulfillment queries and revenue reporting.

Catalog management from the database

Products and ProductVariants updated as Aurora Rows push back to the Shopify storefront.

Abandoned checkout recovery

Abandoned Checkouts replicate to Aurora where recovery jobs query and act on them.

What you can sync between AWS Aurora MySQL and Shopify

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 Shopify objects How this pairing syncs
Views Can serve as read-only sync sources for derived or filtered datasets. Products Catalog entries; often mastered in a PIM or ERP and written into Shopify. Views is specific to AWS Aurora MySQL and Products to Shopify — each maps to any object or custom field on the other side.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. ProductMedias Synced with incremental and full sync per the Stacksync docs. Foreign keys is specific to AWS Aurora MySQL and ProductMedias to Shopify — 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. ProductVariants Synced with incremental and full sync per the Stacksync docs. Stored procedures and triggers is specific to AWS Aurora MySQL and ProductVariants to Shopify — 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. Orders Purchase transactions; pushed to ERPs for fulfillment and billing, and read into databases for reporting. Databases (schemas) is specific to AWS Aurora MySQL and Orders to Shopify — each maps to any object or custom field on the other side.
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. Customers Buyer records; matched to CRM contacts for marketing and lifetime-value analysis. Tables is specific to AWS Aurora MySQL and Customers to Shopify — 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. Abandoned Checkouts Synced with incremental and full sync per the Stacksync docs. Rows is specific to AWS Aurora MySQL and Abandoned Checkouts to Shopify — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL and Shopify

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 Shopify 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 written to Shopify through its API, with automatic retries and rate-limit backoff.

Shopify AWS Aurora MySQL Sub-second propagation

DetectionShopify notifies Stacksync of record changes through webhook events. Webhook topics per resource, with polling on updated_at as a fallback.

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

Rate-limit considerations

  • Shopify: GraphQL uses a calculated query-cost budget; the REST API uses a leaky-bucket model.
What ships with AWS Aurora MySQL ⇄ Shopify

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL or Shopify 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 Shopify 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 Shopify record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ Shopify 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 Shopify.

How the AWS Aurora MySQL and Shopify 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

Shopify

Integration surface
GraphQL Admin API (primary) and REST Admin API (legacy)
Authentication
OAuth via a custom Shopify app: admin creates an app in the Shopify Dev Dashboard, enables required API scopes, sets the Stacksync redirect URL, then supplies shop name + Client ID and Client Secret to Stacksync
Change detection
Webhook topics per resource, with polling on updated_at as a fallback
Capabilities
read · write · webhooks
Rate limits
GraphQL uses a calculated query-cost budget; the REST API uses a leaky-bucket model.
Shopify setup guide
How it works

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

    Choose tables

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

AWS Aurora MySQL and Shopify 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 476 integrations available for AWS Aurora MySQL and Shopify.

Popular · 7 of 476
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