Real-time
Two-way sync
Changes in Dremio or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Dremio to Postgres Heroku integration — real-time, two-way sync
Keep Dremio 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 Dremio and Postgres Heroku
Connect Postgres Heroku and Dremio 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 Dremio, 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 Dremio in real time, and result tables in Dremio sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
- 02 Consolidate data from multiple lake sources through one Dremio semantic layer into a single warehouse target.
- 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
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 Dremio and keep Postgres Heroku focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Postgres Heroku land in Dremio as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between Dremio 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.
| Dremio objects | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| Jobs Query execution records useful for monitoring sync workloads. | Materialized Views Precomputed result sets synced outward on refresh. | Jobs is specific to Dremio and Materialized Views to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Schemas Namespaces that scope which tables a sync reads and writes. | Sources is specific to Dremio and Schemas to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Physical datasets is specific to Dremio and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | Virtual datasets (views) is specific to Dremio and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. | Sequences Generate surrogate keys for rows created by inbound syncs. | Apache Iceberg tables is specific to Dremio and Sequences to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Spaces and folders Namespaces that organize virtual datasets and govern access. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | Spaces and folders is specific to Dremio and Follower Databases to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Dremio 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.
Dremio Postgres Heroku Interval-based propagation
DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.
DeliveryEach detected change is applied to Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Dremio 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 Dremio as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Dremio: Bounded by engine capacity and workload management rather than API rate limits.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Dremio ⇄ Postgres Heroku
Connect Dremio and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dremio–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Dremio 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 Dremio or Postgres Heroku record.
Observability
Monitoring
Track your Dremio ⇄ 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 Dremio and Postgres Heroku.
How the Dremio and Postgres Heroku connectors work
Dremio
- Integration surface
- Arrow Flight SQL, JDBC/ODBC, and a REST API
- Authentication
- Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
- Change detection
- Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Bounded by engine capacity and workload management rather than API rate limits
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 Dremio 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 Dremio 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.
Dremio
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Dremio 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 · Dremio ⇄ 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.
Dremio Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Dremio and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Dremio and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Dremio's Jobs and Sources), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Dremio: Arrow Flight SQL is a first-class endpoint designed for high-throughput columnar result transfer, an alternative to JDBC/ODBC for large extracts. Postgres Heroku: All connections require SSL, and server-level settings such as replication configuration are controlled by Heroku rather than the user. Stacksync's field mapping accounts for these differences between Dremio and Postgres Heroku 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 Dremio 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 Dremio and Postgres Heroku connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Dremio–Postgres Heroku integration in-house.
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Yes — Stacksync ships production-grade connectors for both Dremio 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 Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. 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.
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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