Real-time
Two-way sync
Changes in Amazon S3 or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Storage ⇄ Database
Amazon S3 to Postgres Heroku integration — real-time, two-way sync
Keep Amazon S3 and Postgres Heroku 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
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Amazon S3 and Postgres Heroku
Keep Postgres Heroku and Amazon S3 in step: a row in Postgres Heroku 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 Postgres Heroku 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 JSONB Columns, Sequences, Follower Databases, Tables in Postgres Heroku 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 Keep several Heroku app databases aligned with one system of record
- 02 Reflect billing and subscription records into the app database so product logic reads local 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
A landing zone for incoming files
Files that arrive in a folder or bucket in Amazon S3 become rows in Postgres Heroku 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 Postgres Heroku 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 Postgres Heroku, 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.
What you can sync between Amazon S3 and Postgres Heroku
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 | Postgres Heroku 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. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | Multipart uploads is specific to Amazon S3 and Follower Databases to Postgres Heroku — 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. | Tables Standard Postgres tables; the primary two-way sync target for app data. | Buckets is specific to Amazon S3 and Tables to Postgres Heroku — 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. | Views Read-side projections exposed to outbound syncs. | Objects is specific to Amazon S3 and Views to Postgres Heroku — 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. | Materialized Views Precomputed result sets synced outward on refresh. | Object metadata is specific to Amazon S3 and Materialized Views to Postgres Heroku — 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. | Schemas Namespaces that scope which tables a sync reads and writes. | Object tags is specific to Amazon S3 and Schemas to Postgres Heroku — 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. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Object versions is specific to Amazon S3 and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Amazon S3 and Postgres Heroku
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 Postgres Heroku 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 Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Amazon S3 Interval-based propagation
DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.
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.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Amazon S3 ⇄ Postgres Heroku
Connect Amazon S3 and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Amazon S3 or Postgres Heroku 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 Postgres Heroku record.
Observability
Monitoring
Track your Amazon S3 ⇄ Postgres Heroku 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 Postgres Heroku.
How the Amazon S3 and Postgres Heroku 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.
Postgres Heroku
- Integration surface
- SQL wire protocol (standard PostgreSQL)
- Authentication
- Database credentials from the Heroku DATABASE_URL config var; SSL required
- Change detection
- Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
- Capabilities
- read · write
- Rate limits
- No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works
How to connect Amazon S3 to Postgres Heroku — 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 Postgres Heroku 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
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Amazon S3 and Postgres Heroku 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 ⇄ Postgres HerokuCustomers 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 Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Amazon S3 and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Amazon S3 and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Amazon S3's Multipart uploads and Buckets), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Postgres Heroku side: JSONB Columns, Sequences, Follower Databases, Tables, plus custom fields where Postgres Heroku 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 Postgres Heroku: A landing zone for incoming files; Continuous archival to file storage; A queryable index of the file store. Files that arrive in a folder or bucket in Amazon S3 become rows in Postgres Heroku as they land, so a database-driven process can pick them up without polling the storage system's API.
-
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. Postgres Heroku: SQL wire protocol (standard PostgreSQL). Authentication: Database credentials from the Heroku DATABASE_URL config var; SSL required. Stacksync manages authentication, retries, and rate limits on both sides.
-
Postgres Heroku: Heroku Postgres is standard PostgreSQL, so any Postgres client, driver, or SQL tool connects unchanged. Amazon S3: S3 stores opaque objects, not rows — there is no schema or query language, so listing is done with ListObjectsV2 (1,000 keys per page) and metadata must be indexed externally to be queryable. Stacksync's field mapping accounts for these differences between Amazon S3 and Postgres Heroku 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 439 integrations available for Amazon S3 and Postgres Heroku.
Popular · 7 of 439
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Amazon S3 ⇄ Scaleway Postgres -
Amazon S3 ⇄ TiDB -
Amazon S3 ⇄ TimescaleDB -
Amazon S3 ⇄ VoltDB -
Postgres Heroku ⇄ Egnyte -
Postgres Heroku ⇄ Box -
Postgres Heroku ⇄ Sharepoint
No integrations match .
