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Database ⇄ Data warehouse

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

Keep Amazon Aurora 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon Aurora and Databricks

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

Stacksync covers both directions with one connection. Tables or collections in Amazon Aurora sync into Databricks in real time, and result tables in Databricks sync back into Amazon Aurora, 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 Consolidate several Aurora clusters into one reporting database.
  • 04 Write enriched or scored records from analytics pipelines back into the Aurora tables that power an application.

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 Databricks and keep Amazon Aurora focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon Aurora land in Databricks as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Amazon Aurora 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 Aurora objects Databricks objects How this pairing syncs
Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. 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 Read-only query-backed sources for downstream 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 result sets (PostgreSQL-compatible clusters) readable as sources. 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.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. SQL Warehouses The compute endpoint a sync connects to for query execution. Columns and Data Types is specific to Amazon Aurora and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Primary and Foreign Keys is specific to Amazon Aurora and Change Data Feed to Databricks — each maps to any object or custom field on the other side.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Read Replicas is specific to Amazon Aurora and Catalogs to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Amazon Aurora 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 Aurora Databricks Sub-second propagation

DetectionChanges in Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.

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

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

Rate-limit considerations

  • Amazon Aurora: No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Amazon Aurora ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora 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 Aurora or Databricks record.

Observability

Monitoring

Track your Amazon Aurora ⇄ 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 Aurora and Databricks.

How the Amazon Aurora and Databricks connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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

    Choose tables

    Pick the Amazon Aurora 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 Aurora ⇄ 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 Aurora Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora 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
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GDPR
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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 478 integrations available for Amazon Aurora and Databricks.

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