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Data warehouse

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

Keep Apache Doris and Snowflake 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 Apache Doris and Snowflake

Keep tables consistent across Apache Doris and Snowflake, 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 Snowflake 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 Read aggregates from Doris and sync them into business tools like CRMs or spreadsheets.
  • 02 Consolidate event and transactional data from multiple sources into one real-time OLAP layer.
  • 03 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on
  • 04 Keep a customer 360 table aligned with its source systems in both directions instead of one-way reverse ETL

Common sync patterns

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

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.

What you can sync between Apache Doris and Snowflake

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 Snowflake objects How this pairing syncs
Databases Logical containers that scope connections and grants. Databases Top-level containers that scope which data a sync can touch. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Columnar tables in one of Doris's table models, used as sync destinations. Tables The main landing and activation target for synced records. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Materialized Views Precomputed views readable for downstream syncs and BI. Materialized Views Precomputed results synced outward for low-latency reads. 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. VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. Users and Roles is specific to Apache Doris and VARIANT Columns to Snowflake — 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. Virtual Warehouses The compute a sync's queries run on, sized independently of storage. Unique Key Tables is specific to Apache Doris and Virtual Warehouses to Snowflake — 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. Schemas Namespaces within a database used to organize synced tables. Aggregate Key Tables is specific to Apache Doris and Schemas to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between Apache Doris and Snowflake

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 Snowflake 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 Snowflake as a row-level write, with types converted between the two schemas.

Snowflake Apache Doris Sub-second propagation

DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.

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.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Apache Doris ⇄ Snowflake

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Doris or Snowflake 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 Snowflake record.

Observability

Monitoring

Track your Apache Doris ⇄ Snowflake 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 Snowflake.

How the Apache Doris and Snowflake 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

Snowflake

Integration surface
SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
Authentication
Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
Change detection
Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
Capabilities
read · write · CDC
Rate limits
No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
Snowflake setup guide
How it works

How to connect Apache Doris to Snowflake — 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 Snowflake 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
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Apache Doris and Snowflake 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
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ISO 27001
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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 477 integrations available for Apache Doris and Snowflake.

Popular · 7 of 477
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