Real-time
Two-way sync
Changes in Amazon S3 or Scaleway Postgres instantly reflect in both systems. No stale data, no manual imports.
Storage ⇄ Database
Amazon S3 to Scaleway Postgres integration — real-time, two-way sync
Keep Amazon S3 and Scaleway Postgres 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 Amazon S3 and Scaleway Postgres
Keep Scaleway Postgres and Amazon S3 in step: a row in Scaleway Postgres 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 Scaleway Postgres 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 Sequences, Columns, Tables, Views in Scaleway Postgres with Buckets, Objects, Object metadata, Object tags 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 production tables from Scaleway Postgres into a CRM so sales and support see live product usage on customer records
- 02 Run a two-way sync between Scaleway Postgres and SaaS tools so edits made in either system converge on the same rows
- 03 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.
- 04 Two-way sync Object tags with a database so retention or classification labels set in an internal app write back onto S3 objects via the tagging API without rewriting the files.
Common sync patterns
Records that point at documents
Where a record in Scaleway Postgres 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 Scaleway Postgres as they land, so a database-driven process can pick them up without polling the storage system's API.
Continuous archival to file storage
Rows from Scaleway Postgres are written out to Amazon S3 as files on a schedule or as they change, giving a durable, low-cost copy for backup, compliance, or a data lake, without a custom export job to maintain.
What you can sync between Amazon S3 and Scaleway Postgres
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 | Scaleway Postgres objects | How this pairing syncs | |
|---|---|---|---|
| Prefixes (folders) Logical path segments in object keys used to scope a sync and to parallelize throughput, since S3 rate limits partition by prefix. | Materialized views Precomputed result sets that can be read on a schedule for downstream syncs. | Prefixes (folders) is specific to Amazon S3 and Materialized views to Scaleway Postgres — 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. | Schemas Namespace tables so multiple applications or environments can be synced selectively. | Multipart uploads is specific to Amazon S3 and Schemas to Scaleway Postgres — 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. | Sequences Generate primary keys; sync tooling must respect them when writing rows. | Buckets is specific to Amazon S3 and Sequences to Scaleway Postgres — 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. | Columns Postgres-native types, including JSONB and arrays, are mapped to fields in the paired system. | Objects is specific to Amazon S3 and Columns to Scaleway Postgres — 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. | Tables Primary sync unit; each table maps to an object or table on the other side of the sync. | Object metadata is specific to Amazon S3 and Tables to Scaleway Postgres — 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. | Views Read-only sources for shaping data before it leaves the database. | Object tags is specific to Amazon S3 and Views to Scaleway Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Amazon S3 and Scaleway Postgres
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 Scaleway Postgres 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 Scaleway Postgres as a row-level write, with types converted between the two schemas.
Scaleway Postgres Amazon S3 Sub-second propagation
DetectionChanges in Scaleway Postgres are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication where the managed instance permits it.
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.
- Scaleway Postgres: Throughput is bounded by instance size and connection limits rather than API quotas.
What ships with Amazon S3 ⇄ Scaleway Postgres
Connect Amazon S3 and Scaleway Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–Scaleway Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Amazon S3 or Scaleway Postgres 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 Scaleway Postgres record.
Observability
Monitoring
Track your Amazon S3 ⇄ Scaleway Postgres 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 Scaleway Postgres.
How the Amazon S3 and Scaleway Postgres 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.
Scaleway Postgres
- Integration surface
- SQL wire protocol (PostgreSQL)
- Authentication
- Database credentials (username/password over TLS)
- Change detection
- Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling
- Capabilities
- read · write · CDC
- Rate limits
- Throughput is bounded by instance size and connection limits rather than API quotas
How it works
How to connect Amazon S3 to Scaleway Postgres — 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 Scaleway Postgres 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
Scaleway Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Amazon S3 and Scaleway Postgres 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 ⇄ Scaleway PostgresCustomers 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 Scaleway PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Amazon S3 and Scaleway Postgres integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Amazon S3 and Scaleway Postgres: authenticate both systems, choose the objects to sync (such as Amazon S3's Prefixes (folders) and Multipart uploads), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Scaleway Postgres side: Sequences, Columns, Tables, Views, plus custom fields where Scaleway Postgres exposes them. On the Amazon S3 side: Buckets, Objects, Object metadata, Object tags. 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.
-
Common patterns for Amazon S3 and Scaleway Postgres: Records that point at documents; A landing zone for incoming files; Continuous archival to file storage. Where a record in Scaleway Postgres 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.
-
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. Scaleway Postgres: SQL wire protocol (PostgreSQL). Authentication: Database credentials (username/password over TLS). Stacksync manages authentication, retries, and rate limits on both sides.
-
Scaleway Postgres: Scaleway operates data centers in European regions, which matters for teams with EU data residency requirements. 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 Scaleway Postgres 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 434 integrations available for Amazon S3 and Scaleway Postgres.
Popular · 7 of 434
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Amazon S3 ⇄ IBM AS/400 -
Amazon S3 ⇄ IBM Informix -
Amazon S3 ⇄ InfluxDB -
Amazon S3 ⇄ InterSystems IRIS -
Scaleway Postgres ⇄ Sharepoint -
Scaleway Postgres ⇄ Egnyte -
Scaleway Postgres ⇄ Box
No integrations match .
