Real-time
Two-way sync
Changes in DuckDB or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Database
DuckDB to Postgres Heroku integration — real-time, two-way sync
Keep DuckDB 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 DuckDB and Postgres Heroku
Keep DuckDB and Postgres Heroku synchronized in real time, across engines, regions, or services, in one or both directions.
Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.
Stacksync syncs tables or collections between DuckDB and Postgres Heroku continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.
Common use cases
- 01 Use DuckDB as a transform step: read synced Parquet exports, aggregate with SQL, and write results back to an operational database.
- 02 Sync SaaS data to Parquet on object storage and query it with DuckDB without standing up a warehouse.
- 03 Expose CRM objects as Postgres tables the Heroku application can query and join directly
- 04 Sync Heroku Postgres into a warehouse for reporting without running ETL dynos
Common sync patterns
Regional or environment copies
Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.
Cross-engine sync
Keep the same dataset live in both DuckDB and Postgres Heroku, so each workload runs on the engine that suits it.
Migration with zero-downtime cutover
When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.
What you can sync between DuckDB 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.
| DuckDB objects | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Namespaces within a database used to organize tables in sync outputs. | Schemas Namespaces that scope which tables a sync reads and writes. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Tables Columnar tables created via SQL; the destination for materialized sync data. | Tables Standard Postgres tables; the primary two-way sync target for app data. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Views SQL views used to shape or filter data for downstream consumers. | Views Read-side projections exposed to outbound syncs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| External files (Parquet/CSV/JSON) Files DuckDB queries in place without loading, common as a sync interchange format. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | External files (Parquet/CSV/JSON) is specific to DuckDB and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Attached databases Additional database files or external systems attached into one session for cross-source queries. | Sequences Generate surrogate keys for rows created by inbound syncs. | Attached databases is specific to DuckDB and Sequences to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | Database files is specific to DuckDB and Follower Databases to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between DuckDB 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.
DuckDB Postgres Heroku Interval-based propagation
DetectionStacksync polls DuckDB for changes on an incremental schedule, reading only records changed since the previous pass. Polling or full re-reads.
DeliveryEach detected change is applied to Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku DuckDB 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 applied to DuckDB as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- DuckDB: No API rate limits; throughput is bounded by local compute and I/O.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with DuckDB ⇄ Postgres Heroku
Connect DuckDB and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every DuckDB–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever DuckDB 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 DuckDB or Postgres Heroku record.
Observability
Monitoring
Track your DuckDB ⇄ 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 DuckDB and Postgres Heroku.
How the DuckDB and Postgres Heroku connectors work
DuckDB
- Integration surface
- In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
- Authentication
- None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
- Change detection
- Polling or full re-reads; no change feed or transaction log API
- Capabilities
- read · write
- Rate limits
- No API rate limits; throughput is bounded by local compute and I/O
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 DuckDB 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 DuckDB 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.
DuckDB
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the DuckDB 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 · DuckDB ⇄ 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.
DuckDB Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
DuckDB and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between DuckDB and Postgres Heroku: authenticate both systems, choose the objects to sync (such as DuckDB's Schemas and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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 DuckDB and Postgres Heroku 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 DuckDB and Postgres Heroku connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom DuckDB–Postgres Heroku integration in-house.
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Yes — Stacksync ships production-grade connectors for both DuckDB and Postgres Heroku. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on DuckDB: Polling or full re-reads; no change feed or transaction log API. On Postgres Heroku: Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the DuckDB side: Schemas, Tables, Views, External files (Parquet/CSV/JSON), plus custom fields where DuckDB exposes them. On the Postgres Heroku side: Sequences, Follower Databases, Tables, Views. Stacksync auto-detects both schemas and converts types between the two systems.
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 392 integrations available for DuckDB and Postgres Heroku.
Popular · 7 of 392
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DuckDB ⇄ Supabase -
DuckDB ⇄ MySQL -
DuckDB ⇄ MariaDB -
Postgres Heroku ⇄ Dynamo DB -
Postgres Heroku ⇄ IBM Db2 -
Postgres Heroku ⇄ Firebase -
Postgres Heroku ⇄ SingleStore
No integrations match .
