Real-time
Two-way sync
Changes in AWS S3 or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
AWS S3 to Postgres Heroku integration — real-time, two-way sync
Keep AWS 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
- 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 AWS S3 and Postgres Heroku
Connect Postgres Heroku and AWS S3 with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Postgres Heroku's rows in AWS S3, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Postgres Heroku where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Postgres Heroku sync into AWS S3 in real time, and result tables in AWS S3 sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.
Common use cases
- Stage bulk loads for warehouses that ingest from object storage.
- Archive change history from ongoing syncs as timestamped files for audit and replay.
- Expose CRM objects as Postgres tables the Heroku application can query and join directly
- Sync Heroku Postgres into a warehouse for reporting without running ETL dynos
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Offload heavy reads
Point analytical queries at the synced copy in AWS S3 and keep Postgres Heroku focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Postgres Heroku land in AWS S3 as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between AWS 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.
| AWS S3 objects | Postgres Heroku objects | |
|---|---|---|
| Object Metadata System and user-defined metadata read alongside object contents. | Views Read-side projections exposed to outbound syncs. | |
| Object Versions Prior copies retained when versioning is enabled, relevant for reprocessing. | Materialized Views Precomputed result sets synced outward on refresh. | |
| Event Notifications Notifications on object creation or deletion that trigger incremental processing. | Schemas Namespaces that scope which tables a sync reads and writes. | |
| Access Points Scoped network endpoints used to grant a sync narrow access to a bucket. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | |
| Multipart Uploads The mechanism used to write large export files reliably. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | |
| Buckets Top-level containers a sync targets; region and policy are set at this level. | Sequences Generate surrogate keys for rows created by inbound syncs. |
What ships with AWS S3 ⇄ Postgres Heroku
Connect AWS S3 and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS S3–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever AWS 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 AWS S3 or Postgres Heroku record.
Observability
Monitoring
Track your AWS 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 AWS S3 and Postgres Heroku.
How the AWS S3 and Postgres Heroku connectors work
AWS S3
- Integration surface
- REST API (the S3 API), accessed directly or through AWS SDKs
- Authentication
- AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes
- Change detection
- S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge; list-based polling as a fallback
- Capabilities
- read · write · webhooks
- Rate limits
- Request throughput scales per prefix; sustained high-volume workloads should spread keys across prefixes
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 AWS 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 AWS 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.
AWS S3
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the AWS 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 · AWS 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.
AWS S3 Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
AWS S3 and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between AWS S3 and Postgres Heroku: authenticate both systems, choose the objects to sync (such as AWS S3's Object Metadata and Object Versions), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the AWS S3 side: Prefixes, Object Metadata, Object Versions, Event Notifications, plus custom fields where AWS S3 exposes them. On the Postgres Heroku side: Primary and Unique Keys, JSONB Columns, Sequences, Follower Databases. 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 AWS S3 and Postgres Heroku: Fresh analytics without loading windows; Offload heavy reads; Operational data in the warehouse, minus the pipeline. Because changes stream continuously, analysts query current data instead of waiting for last night's load.
-
AWS S3: REST API (the S3 API), accessed directly or through AWS SDKs. Authentication: AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes. 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.
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AWS S3: S3 provides strong read-after-write consistency for all operations, so newly written objects are immediately readable by a sync. Postgres Heroku: Heroku Postgres is standard PostgreSQL, so any Postgres client, driver, or SQL tool connects unchanged. Stacksync's field mapping accounts for these differences between AWS S3 and Postgres Heroku without custom code.
SECURITY
Security teams love 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
CSA STAR
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 386 integrations available for AWS S3 and Postgres Heroku.
Popular · 8 of 386
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AWS S3 ⇄ Azure SQL Database -
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Postgres Heroku ⇄ Snowflake -
Postgres Heroku ⇄ BigQuery -
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Postgres Heroku ⇄ Databricks
No integrations match .
