Real-time
Two-way sync
Changes in Amazon S3 or Render Postgres instantly reflect in both systems. No stale data, no manual imports.
Storage ⇄ Database
Amazon S3 to Render Postgres integration — real-time, two-way sync
Keep Amazon S3 and Render 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.
- 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 Amazon S3 and Render Postgres
Keep Render Postgres and Amazon S3 in step: a row in Render 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 Render 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 Indexes and Constraints, Tables, Views, Materialized Views in Render 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 Replicate between a Render database and another Postgres or warehouse for migration or environment separation.
- 02 Two-way sync CRM or ERP objects into Render Postgres tables so product and ops teams query business data with plain SQL.
- 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
Continuous archival to file storage
Rows from Render 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.
A queryable index of the file store
Every file or object in Amazon S3 shows up as a row in Render Postgres, 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 Render Postgres 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.
What you can sync between Amazon S3 and Render 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 | Render 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. | Tables Relational tables with full column typing; synced two-way with CRMs, ERPs, and SaaS apps so application data is queryable as plain Postgres rows. | Prefixes (folders) is specific to Amazon S3 and Tables to Render 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. | Views Saved queries exposed as read-only relations; read out to BI tools or downstream syncs without duplicating transformation logic. | Multipart uploads is specific to Amazon S3 and Views to Render 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. | Materialized Views Precomputed query results refreshed on demand; read for fast reporting tables that downstream systems can consume. | Buckets is specific to Amazon S3 and Materialized Views to Render 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. | Schemas Namespaces that organize tables per app or environment; sync targets are scoped per schema to keep synced data isolated and tidy. | Objects is specific to Amazon S3 and Schemas to Render 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. | Columns and Types Full Postgres type system including JSONB and arrays; field mappings preserve native types instead of flattening to strings. | Object metadata is specific to Amazon S3 and Columns and Types to Render 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. | Indexes and Constraints Primary keys, unique constraints, and foreign keys; unique keys drive idempotent upserts and conflict resolution during sync. | Object tags is specific to Amazon S3 and Indexes and Constraints to Render Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Amazon S3 and Render 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 Render 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 Render Postgres as a row-level write, with types converted between the two schemas.
Render Postgres Amazon S3 Sub-second propagation
DetectionChanges in Render Postgres are captured at the source via change data capture — no polling loop against its API. Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the.
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.
- Render Postgres: No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
What ships with Amazon S3 ⇄ Render Postgres
Connect Amazon S3 and Render Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–Render Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Amazon S3 or Render 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 Render Postgres record.
Observability
Monitoring
Track your Amazon S3 ⇄ Render 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 Render Postgres.
How the Amazon S3 and Render 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.
Render Postgres
- Integration surface
- PostgreSQL wire protocol (managed Postgres on Render)
- Authentication
- Standard Postgres connection string — host, port, database, user, password with TLS; Render provides internal and external connection URLs and IP allowlisting
- Change detection
- Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the fallback
- Capabilities
- read · write · CDC
- Rate limits
- No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
How it works
How to connect Amazon S3 to Render 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 Render 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
Render Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Amazon S3 and Render 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 ⇄ Render 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 Render PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Amazon S3 and Render Postgres integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Amazon S3 and Render 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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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. Render Postgres: PostgreSQL wire protocol (managed Postgres on Render). Authentication: Standard Postgres connection string — host, port, database, user, password with TLS; Render provides internal and external connection URLs and IP allowlisting. Stacksync manages authentication, retries, and rate limits on both sides.
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Render Postgres: Render Postgres is standard PostgreSQL — anything that speaks the Postgres protocol works unchanged, including logical replication clients. Amazon S3: Objects can be up to 5 TB, but a single PUT is capped at 5 GB, so larger files must use multipart upload, and objects in Glacier storage classes must be restored before their bytes can be read. Stacksync's field mapping accounts for these differences between Amazon S3 and Render Postgres without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Amazon S3 and Render Postgres records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Amazon S3 and Render Postgres connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Amazon S3–Render Postgres integration in-house.
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Yes — Stacksync ships production-grade connectors for both Amazon S3 and Render Postgres. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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 407 integrations available for Amazon S3 and Render Postgres.
Popular · 7 of 407
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Amazon S3 ⇄ IBM Db2 -
Amazon S3 ⇄ Firebase -
Amazon S3 ⇄ SingleStore -
Amazon S3 ⇄ Amazon Aurora -
Render Postgres ⇄ Egnyte -
Render Postgres ⇄ Box -
Render Postgres ⇄ Sharepoint
No integrations match .
