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Data warehouse ⇄ Database

Databricks to PostgreSQL integration — real-time, two-way sync

Keep Databricks and PostgreSQL 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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Why teams connect Databricks and PostgreSQL

Connect PostgreSQL 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 PostgreSQL'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 PostgreSQL where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in PostgreSQL sync into Databricks in real time, and result tables in Databricks sync back into PostgreSQL, with schema and type mapping between the two systems handled for you.

Common use cases

  • Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • Feed reporting and BI from a continuously synced Postgres replica instead of scheduled ETL scripts
  • Expose SaaS objects (CRM contacts, ERP invoices, support tickets) as Postgres tables that internal tools can query and join

Offload heavy reads

Point analytical queries at the synced copy in Databricks and keep PostgreSQL focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from PostgreSQL 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 PostgreSQL, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Databricks and PostgreSQL

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 PostgreSQL objects
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Columns Field-level mapping targets; types are mapped to the connected system's field types.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Sequences Generate surrogate keys for rows created by inbound syncs.
Views Curated read-only projections used as sync sources for downstream tools. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems.
What ships with Databricks ⇄ PostgreSQL

Connect Databricks and PostgreSQL for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–PostgreSQL connection.

Real-time

Two-way sync

Changes in Databricks or PostgreSQL instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or PostgreSQL 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 PostgreSQL record.

Observability

Monitoring

Track your Databricks ⇄ PostgreSQL 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 PostgreSQL.

How the Databricks and PostgreSQL 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

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

How to connect Databricks to PostgreSQL — 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.

  1. 01

    Connect your apps

    Authenticate Databricks and PostgreSQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and PostgreSQL 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ PostgreSQL
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Databricks PostgreSQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and PostgreSQL integration FAQ

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.

Learn more about security
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and PostgreSQL.

Popular · 8 of 386
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