Real-time
Two-way sync
Changes in Apache Doris or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Apache Doris to Postgres Heroku integration — real-time, two-way sync
Keep Apache Doris 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.
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Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
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Migrated from Heroku Connect
Migrated from Matillion
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Apache Doris and Postgres Heroku
Connect Postgres Heroku and Apache Doris 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 Apache Doris, 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 Apache Doris in real time, and result tables in Apache Doris sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Read aggregates from Doris and sync them into business tools like CRMs or spreadsheets.
- 02 Consolidate event and transactional data from multiple sources into one real-time OLAP layer.
- 03 Sync Heroku Postgres into a warehouse for reporting without running ETL dynos
- 04 Keep several Heroku app databases aligned with one system of record
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Apache Doris and keep Postgres Heroku focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Postgres Heroku land in Apache Doris as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Apache Doris sync into Postgres Heroku, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Apache Doris 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.
| Apache Doris objects | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| Tables Columnar tables in one of Doris's table models, used as sync destinations. | 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. | |
| Materialized Views Precomputed views readable for downstream syncs and BI. | Materialized Views Precomputed result sets synced outward on refresh. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Databases Logical containers that scope connections and grants. | Schemas Namespaces that scope which tables a sync reads and writes. | Databases is specific to Apache Doris and Schemas to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Unique Key Tables Tables supporting primary-key upserts, the natural target for row-level syncs. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Unique Key Tables is specific to Apache Doris and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Aggregate Key Tables Tables that pre-aggregate on load, used for metric rollups. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | Aggregate Key Tables is specific to Apache Doris and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Partitions Range or list partitions that bound incremental loads. | Sequences Generate surrogate keys for rows created by inbound syncs. | Partitions is specific to Apache Doris and Sequences to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Apache Doris 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.
Apache Doris Postgres Heroku Interval-based propagation
DetectionStacksync polls Apache Doris for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition or timestamp columns for reads.
DeliveryEach detected change is applied to Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Apache Doris 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 Apache Doris as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Apache Doris: No API quotas; load throughput depends on cluster resources and load-job configuration.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Apache Doris ⇄ Postgres Heroku
Connect Apache Doris and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Doris–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Doris 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 Apache Doris or Postgres Heroku record.
Observability
Monitoring
Track your Apache Doris ⇄ 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 Apache Doris and Postgres Heroku.
How the Apache Doris and Postgres Heroku connectors work
Apache Doris
- Integration surface
- MySQL wire protocol for SQL access; HTTP APIs (such as Stream Load) for bulk ingestion
- Authentication
- Database credentials
- Change detection
- Polling on partition or timestamp columns for reads; ingestion into Doris is push-based via load jobs
- Capabilities
- read · write
- Rate limits
- No API quotas; load throughput depends on cluster resources and load-job configuration
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 Apache Doris 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 Apache Doris 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.
Apache Doris
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Doris 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 · Apache Doris ⇄ 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.
Apache Doris Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Doris and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Doris and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Apache Doris's Tables and Materialized Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Doris and Postgres Heroku connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Doris–Postgres Heroku integration in-house.
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Yes — Stacksync ships production-grade connectors for both Apache Doris 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 Apache Doris: Polling on partition or timestamp columns for reads; ingestion into Doris is push-based via load jobs. 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 Apache Doris side: Unique Key Tables, Aggregate Key Tables, Partitions, Materialized Views, plus custom fields where Apache Doris exposes them. On the Postgres Heroku side: Sequences, Follower Databases, Tables, Views. 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.
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
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