Real-time
Two-way sync
Changes in Amazon S3 or AWS Aurora PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
Storage ⇄ Database
Amazon S3 to AWS Aurora PostgreSQL integration — real-time, two-way sync
Keep Amazon S3 and AWS Aurora PostgreSQL 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
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Amazon S3 and AWS Aurora PostgreSQL
Keep AWS Aurora PostgreSQL and Amazon S3 in step: a row in AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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 Foreign keys, Replication slots and publications, Databases and schemas, Tables in AWS Aurora PostgreSQL with Multipart uploads, Buckets, Objects, Object metadata 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 Sync JSONB-heavy application data into structured objects in downstream business systems.
- 02 Keep a customer-facing Aurora database aligned with an internal admin tool, with writes accepted on both sides.
- 03 Trigger a database or ERP record update the moment an S3 Event Notification fires an object-created event, for example when a partner drops an EDI or invoice file into an inbound prefix.
- 04 Reconcile Object versions and delete markers into an audit table so teams track when files under a compliance prefix were added, replaced, or removed.
Common sync patterns
A queryable index of the file store
Every file or object in Amazon S3 shows up as a row in AWS Aurora PostgreSQL, with its name, folder or key, size, type, and modified date as columns, so the contents of the store can be listed, filtered, and joined like any other table.
File metadata kept in step
Tags, custom properties, owner, or status columns edited on a row in AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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.
What you can sync between Amazon S3 and AWS Aurora PostgreSQL
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 | AWS Aurora PostgreSQL objects | How this pairing syncs | |
|---|---|---|---|
| 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. | Tables The core sync unit; rows are matched across systems by primary key. | Object tags is specific to Amazon S3 and Tables to AWS Aurora PostgreSQL — 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. | Rows Inserted, updated, and deleted in both directions during bi-directional syncs. | Object versions is specific to Amazon S3 and Rows to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Prefixes (folders) Logical path segments in object keys used to scope a sync and to parallelize throughput, since S3 rate limits partition by prefix. | Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | Prefixes (folders) is specific to Amazon S3 and Columns to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| 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. | Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | Multipart uploads is specific to Amazon S3 and Primary keys and constraints to AWS Aurora PostgreSQL — 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. | Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. | Buckets is specific to Amazon S3 and Views and materialized views to AWS Aurora PostgreSQL — 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. | Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | Objects is specific to Amazon S3 and Foreign keys to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. |
How changes propagate between Amazon S3 and AWS Aurora PostgreSQL
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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.
AWS Aurora PostgreSQL Amazon S3 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 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.
What ships with Amazon S3 ⇄ AWS Aurora PostgreSQL
Connect Amazon S3 and AWS Aurora PostgreSQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–AWS Aurora PostgreSQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Amazon S3 or AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL record.
Observability
Monitoring
Track your Amazon S3 ⇄ AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL.
How the Amazon S3 and AWS Aurora PostgreSQL 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.
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
How it works
How to connect Amazon S3 to AWS Aurora PostgreSQL — 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.
- 01
Connect your apps
Authenticate Amazon S3 and AWS Aurora PostgreSQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Amazon S3
connected
AWS Aurora PostgreSQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Amazon S3 and AWS Aurora PostgreSQL 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.
objects · Amazon S3 ⇄ AWS Aurora PostgreSQLCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Amazon S3 AWS Aurora PostgreSQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Amazon S3 and AWS Aurora PostgreSQL integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Amazon S3 and AWS Aurora PostgreSQL: authenticate both systems, choose the objects to sync (such as Amazon S3's Object tags and Object versions), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the AWS Aurora PostgreSQL side: Foreign keys, Replication slots and publications, Databases and schemas, Tables, plus custom fields where AWS Aurora PostgreSQL exposes them. On the Amazon S3 side: Multipart uploads, Buckets, Objects, Object metadata. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Amazon S3 and AWS Aurora PostgreSQL: A queryable index of the file store; File metadata kept in step; Records that point at documents. Every file or object in Amazon S3 shows up as a row in AWS Aurora PostgreSQL, with its name, folder or key, size, type, and modified date as columns, so the contents of the store can be listed, filtered, and joined like any other table.
-
Amazon S3: 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. AWS Aurora PostgreSQL: SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC. Authentication: Database credentials, optionally AWS IAM database authentication, over TLS. Stacksync manages authentication, retries, and rate limits on both sides.
-
AWS Aurora PostgreSQL: Logical replication uses publications and replication slots, so CDC reads changes from the write-ahead log without polling production tables. Amazon S3: Access is scoped per bucket via IAM and bucket policies; a two-way sync credential needs s3:GetObject, s3:PutObject, s3:ListBucket, and object-tagging permissions. Stacksync's field mapping accounts for these differences between Amazon S3 and AWS Aurora PostgreSQL without custom code.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 443 integrations available for Amazon S3 and AWS Aurora PostgreSQL.
Popular · 7 of 443
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Amazon S3 ⇄ Azure Cosmos DB -
Amazon S3 ⇄ Azure SQL Database -
Amazon S3 ⇄ Citus -
Amazon S3 ⇄ Couchbase -
AWS Aurora PostgreSQL ⇄ Box -
AWS Aurora PostgreSQL ⇄ Sharepoint -
AWS Aurora PostgreSQL ⇄ Egnyte
No integrations match .
