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Database ⇄ Data warehouse

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

Keep AWS Aurora PostgreSQL and Databricks 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

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Why teams connect AWS Aurora PostgreSQL and Databricks

Connect AWS Aurora 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 AWS Aurora 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 AWS Aurora 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 AWS Aurora PostgreSQL sync into Databricks in real time, and result tables in Databricks sync back into AWS Aurora 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.
  • Sync JSONB-heavy application data into structured objects in downstream business systems.
  • Keep a customer-facing Aurora database aligned with an internal admin tool, with writes accepted on both sides.

Operational data in the warehouse, minus the pipeline

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

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

What you can sync between AWS Aurora PostgreSQL and Databricks

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.

AWS Aurora PostgreSQL objects Databricks objects
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address.
Tables The core sync unit; rows are matched across systems by primary key. Schemas Group tables and views; syncs typically target a dedicated schema per source system.
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. Delta Tables The primary read and write target; operational data lands here as managed or external tables.
Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. Views Curated read-only projections used as sync sources for downstream tools.
Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs.
Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. Volumes Unity Catalog file storage used for staging bulk loads.
What ships with AWS Aurora PostgreSQL ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL or Databricks 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 AWS Aurora PostgreSQL or Databricks record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ Databricks sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and Databricks.

How the AWS Aurora PostgreSQL and Databricks connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC

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
How it works

How to connect AWS Aurora PostgreSQL to Databricks — 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 AWS Aurora PostgreSQL and Databricks 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
    AWS Aurora PostgreSQL connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL and Databricks 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 · AWS Aurora PostgreSQL ⇄ Databricks
    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
    AWS Aurora PostgreSQL Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora PostgreSQL and Databricks 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 AWS Aurora PostgreSQL and Databricks.

Popular · 8 of 386
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