Skip to content
Storage ⇄ Database

Amazon S3 to MySQL integration — real-time, two-way sync

Keep Amazon S3 and MySQL 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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Amazon S3 and MySQL

Keep MySQL and Amazon S3 in step: a row in MySQL for every file in Amazon S3, with names, paths, metadata, and status staying consistent in real time, in both directions.

A database holds structured records; a storage system holds the files those records depend on, such as contracts, images, exports, uploads, and documents. The two describe the same things from opposite sides: a row in MySQL says a file exists and carries its name, location, and status, while Amazon S3 holds the bytes. Linked only by a hand-kept path or a one-off script, the two drift the moment a file is renamed, moved, or deleted and the record still points at where it used to be.

Stacksync syncs Tables, Views, Columns, Primary and Unique Keys in MySQL with Object tags, Object versions, Prefixes (folders), Multipart uploads in Amazon S3 bi-directionally and in real time. File attributes, including name, path or object key, size, type, modified time, owner, and tags or custom properties, map field by field to columns on the matching row, and a change on either side shows up on the other within seconds. New files appear as rows, metadata edits travel in the direction you choose, and deletes stay consistent, with conflict rules you set in place of nightly reconciliation scripts.

Common use cases

  • 01 Keep e-commerce orders in MySQL flowing into finance and fulfillment systems without export scripts
  • 02 Feed a warehouse from MySQL continuously using binlog-based capture instead of nightly dumps
  • 03 Reconcile Object versions and delete markers into an audit table so teams track when files under a compliance prefix were added, replaced, or removed.
  • 04 Index every new Object's key, size, LastModified, and user metadata into a Postgres catalog table so applications query S3 contents in SQL instead of paging ListObjectsV2.

Common sync patterns

File metadata kept in step

Tags, custom properties, owner, or status columns edited on a row in MySQL write back to the matching file's metadata in Amazon S3, and metadata changed in Amazon S3 updates the row, so the two never disagree about a file.

Records that point at documents

Where a record in MySQL references a file in Amazon S3, such as a contract, an image, an export, or an upload, the reference, path, and status stay consistent as files are renamed, moved, or replaced, so stored links keep resolving.

A landing zone for incoming files

Files that arrive in a folder or bucket in Amazon S3 become rows in MySQL as they land, so a database-driven process can pick them up without polling the storage system's API.

What you can sync between Amazon S3 and MySQL

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 S3 objects MySQL objects How this pairing syncs
Multipart uploads In-progress large-object uploads assembled from parts; objects above ~100 MB (required above 5 GB) are written this way, and incomplete uploads persist until completed or aborted. Columns Field-level mapping targets with engine-typed values. Multipart uploads is specific to Amazon S3 and Columns to MySQL — each maps to any object or custom field on the other side.
Buckets Top-level, region-scoped containers that hold objects; enumerated to discover the namespaces and prefixes a sync should cover. Primary and Unique Keys Match keys for idempotent upserts and conflict handling. Buckets is specific to Amazon S3 and Primary and Unique Keys to MySQL — each maps to any object or custom field on the other side.
Objects Files stored under a key; content is read with GET and written with PUT, and each object's key/size/ETag/LastModified is the unit indexed into a database. JSON Columns Validated semi-structured payloads for nested SaaS data. Objects is specific to Amazon S3 and JSON Columns to MySQL — each maps to any object or custom field on the other side.
Object metadata System metadata (Content-Type, size, ETag, LastModified) plus user-defined x-amz-meta-* headers; user metadata is fixed at write time and only changeable by rewriting the object. Stored Procedures Server-side logic that can post-process synced rows. Object metadata is specific to Amazon S3 and Stored Procedures to MySQL — each maps to any object or custom field on the other side.
Object tags Up to 10 key-value tags per object, mutable in place via the tagging API independent of content, so classification and retention labels sync two-way without rewriting files. Triggers An alternative change-capture mechanism when binlog access is unavailable. Object tags is specific to Amazon S3 and Triggers to MySQL — each maps to any object or custom field on the other side.
Object versions When bucket versioning is enabled every write creates a new version ID; prior versions and delete markers are readable for history and audit syncs. Databases (Schemas) Top-level namespaces that scope a sync's reads and writes. Object versions is specific to Amazon S3 and Databases (Schemas) to MySQL — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and MySQL

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.

Amazon S3 MySQL Sub-second propagation

DetectionAmazon S3 notifies Stacksync of record changes through webhook events. S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge.

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

MySQL Amazon S3 Sub-second propagation

DetectionChanges in MySQL are captured at the source via change data capture — no polling loop against its API. Database triggers — Stacksync creates deterministic triggers for internal logging and syncing (requires log_bin_trust_function_creators=ON when.

DeliveryEach detected change is written to Amazon S3 through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Amazon S3: S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.
  • MySQL: No API rate limits; throughput is bounded by connection limits and server resources.
What ships with Amazon S3 ⇄ MySQL

Connect Amazon S3 and MySQL for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–MySQL connection.

Real-time

Two-way sync

Changes in Amazon S3 or MySQL instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon S3 or MySQL 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 S3 or MySQL record.

Observability

Monitoring

Track your Amazon S3 ⇄ MySQL sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon S3 and MySQL.

How the Amazon S3 and MySQL connectors work

Amazon S3

Integration surface
S3 REST API (also via AWS SDKs and the S3-compatible endpoint)
Authentication
AWS IAM credentials — an access key ID and secret access key signed with AWS Signature Version 4; supports temporary STS credentials and cross-account IAM roles
Change detection
S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge; there is no modified-since query, so polling relies on each object's LastModified from ListObjectsV2
Capabilities
read · write · webhooks
Rate limits
S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.

MySQL

Integration surface
SQL wire protocol (MySQL client/server protocol)
Authentication
Database credentials entered as a connection string or parameters, with optional SSL root certificate upload and optional SSH tunnel (SSH user + SSH host)
Change detection
Database triggers — Stacksync creates deterministic triggers for internal logging and syncing (requires log_bin_trust_function_creators=ON when binary logging is enabled)
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits and server resources
MySQL setup guide
How it works

How to connect Amazon S3 to MySQL — 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 S3 and MySQL 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 S3 connected
    MySQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon S3 and MySQL 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 S3 ⇄ MySQL
    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 S3 MySQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Amazon S3 and MySQL 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 537 integrations available for Amazon S3 and MySQL.

Popular · 7 of 537
All Amazon S3 integrations All MySQL integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own