Skip to content
Data warehouse

Amazon Redshift to Databricks integration — real-time, two-way sync

Keep Amazon Redshift 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

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 Amazon Redshift and Databricks

Keep tables consistent across Amazon Redshift and Databricks, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between Amazon Redshift and Databricks continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • 01 Publish finance rollups computed in Redshift back to spreadsheets or operational tools.
  • 02 Feed customer 360 tables built in Redshift to support and success platforms.
  • 03 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 04 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.

Common sync patterns

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

Migration without a big bang

When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

What you can sync between Amazon Redshift 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.

Amazon Redshift objects Databricks objects How this pairing syncs
Schemas Namespaces used to organize synced tables and control grants. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views SQL views readable as modeled sources for reverse syncs. Views Curated read-only projections used as sync sources for downstream tools. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Materialized Views Precomputed results that downstream syncs can read for performance. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Stored Procedures SQL procedures sometimes invoked around load steps. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Stored Procedures is specific to Amazon Redshift and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Users and Groups Principals used to grant a sync connection scoped access. Volumes Unity Catalog file storage used for staging bulk loads. Users and Groups is specific to Amazon Redshift and Volumes to Databricks — each maps to any object or custom field on the other side.
Databases Top-level containers within a cluster or serverless workgroup. SQL Warehouses The compute endpoint a sync connects to for query execution. Databases is specific to Amazon Redshift and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Amazon Redshift and Databricks

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.

Amazon Redshift Databricks Interval-based propagation

DetectionStacksync polls Amazon Redshift for changes on an incremental schedule, reading only records changed since the previous pass. Polling or query-based diffing.

DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.

Databricks Amazon Redshift 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 Amazon Redshift as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon Redshift: Bounded by cluster or serverless capacity and concurrency settings rather than API quotas.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Amazon Redshift ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Redshift 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 Amazon Redshift or Databricks record.

Observability

Monitoring

Track your Amazon Redshift ⇄ Databricks sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Redshift and Databricks.

How the Amazon Redshift and Databricks connectors work

Amazon Redshift

Integration surface
SQL over JDBC/ODBC (PostgreSQL-derived protocol); Redshift Data API over HTTPS
Authentication
Database credentials or IAM-based authentication
Change detection
Polling or query-based diffing; Redshift does not expose a transaction log for external CDC consumers
Capabilities
read · write
Rate limits
Bounded by cluster or serverless capacity and concurrency settings rather than API quotas

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 Amazon Redshift 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 Amazon Redshift 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
    Amazon Redshift connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Redshift 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 · Amazon Redshift ⇄ 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
    Amazon Redshift Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Amazon Redshift and Databricks integration FAQ

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.

Learn more about security
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:

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 586 integrations available for Amazon Redshift and Databricks.

Popular · 7 of 586
All Amazon Redshift integrations All Databricks integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own