Real-time
Two-way sync
Changes in Databricks or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Databricks to Postgres Heroku integration — real-time, two-way sync
Keep Databricks 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
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Migrated from Fivetran
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Why teams connect Databricks and Postgres Heroku
Connect Postgres Heroku and Databricks 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 Databricks, 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 Databricks in real time, and result tables in Databricks sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- 03 Reflect billing and subscription records into the app database so product logic reads local rows
- 04 Expose CRM objects as Postgres tables the Heroku application can query and join directly
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Databricks and keep Postgres Heroku focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Postgres Heroku land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Databricks sync into Postgres Heroku, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Databricks 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.
| Databricks objects | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | 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. | |
| Views Curated read-only projections used as sync sources for downstream tools. | 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. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | 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. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Tables Standard Postgres tables; the primary two-way sync target for app data. | Delta Tables is specific to Databricks and Tables to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Volumes is specific to Databricks and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | SQL Warehouses is specific to Databricks and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Databricks 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.
Databricks Postgres Heroku Sub-second propagation
DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.
DeliveryEach detected change is applied to Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Databricks 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 Databricks as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Databricks ⇄ Postgres Heroku
Connect Databricks and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks 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 Databricks or Postgres Heroku record.
Observability
Monitoring
Track your Databricks ⇄ 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 Databricks and Postgres Heroku.
How the Databricks and Postgres Heroku connectors work
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace 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 Databricks 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 Databricks 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.
Databricks
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks 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 · Databricks ⇄ 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.
Databricks Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Databricks's Schemas and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Databricks side: Materialized Views, Volumes, SQL Warehouses, Change Data Feed, plus custom fields where Databricks exposes them. On the Postgres Heroku side: Follower Databases, Tables, Views, Materialized 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.
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Common patterns for Databricks and Postgres Heroku: Offload heavy reads; Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed. Point analytical queries at the synced copy in Databricks and keep Postgres Heroku focused on its operational workload.
-
Databricks: SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution. Authentication: Personal access tokens or OAuth machine-to-machine credentials for service principals. 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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Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. 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 Databricks 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
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Postgres Heroku ⇄ Rockset
No integrations match .
