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Data warehouse

Apache Doris to Databricks integration — real-time, two-way sync

Keep Apache Doris 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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  • 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 Apache Doris and Databricks

Keep tables consistent across Apache Doris 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 Apache Doris 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 Continuously upsert changing records into Unique Key tables so analytics reflect current state rather than append-only history.
  • 02 Read aggregates from Doris and sync them into business tools like CRMs or spreadsheets.
  • 03 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 04 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.

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 Apache Doris 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.

Apache Doris objects Databricks objects How this pairing syncs
Materialized Views Precomputed views readable for downstream syncs and BI. 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.
Users and Roles Principals used to grant the sync connection scoped access. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Users and Roles is specific to Apache Doris and Schemas to Databricks — each maps to any object or custom field on the other side.
Databases Logical containers that scope connections and grants. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Databases is specific to Apache Doris and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Tables Columnar tables in one of Doris's table models, used as sync destinations. Views Curated read-only projections used as sync sources for downstream tools. Tables is specific to Apache Doris and Views to Databricks — each maps to any object or custom field on the other side.
Unique Key Tables Tables supporting primary-key upserts, the natural target for row-level syncs. Volumes Unity Catalog file storage used for staging bulk loads. Unique Key Tables is specific to Apache Doris and Volumes to Databricks — each maps to any object or custom field on the other side.
Aggregate Key Tables Tables that pre-aggregate on load, used for metric rollups. SQL Warehouses The compute endpoint a sync connects to for query execution. Aggregate Key Tables is specific to Apache Doris and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Apache Doris 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.

Apache Doris Databricks Interval-based propagation

DetectionStacksync polls Apache Doris for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition or timestamp columns for reads.

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

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

Rate-limit considerations

  • Apache Doris: No API quotas; load throughput depends on cluster resources and load-job configuration.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Apache Doris ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Doris 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 Apache Doris or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Doris and Databricks.

How the Apache Doris and Databricks connectors work

Apache Doris

Integration surface
MySQL wire protocol for SQL access; HTTP APIs (such as Stream Load) for bulk ingestion
Authentication
Database credentials
Change detection
Polling on partition or timestamp columns for reads; ingestion into Doris is push-based via load jobs
Capabilities
read · write
Rate limits
No API quotas; load throughput depends on cluster resources and load-job configuration

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

    Choose tables

    Pick the Apache Doris 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 · Apache Doris ⇄ 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
    Apache Doris Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Doris 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.

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→ 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 480 integrations available for Apache Doris and Databricks.

Popular · 5 of 480
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