Real-time
Two-way sync
Changes in DuckDB or Snowflake instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
DuckDB to Snowflake integration — real-time, two-way sync
Keep DuckDB 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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Why teams connect DuckDB and Snowflake
Connect DuckDB and Snowflake 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 DuckDB's rows in Snowflake, 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 DuckDB where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in DuckDB sync into Snowflake in real time, and result tables in Snowflake sync back into DuckDB, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
- 02 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on
- 03 Sync SaaS data to Parquet on object storage and query it with DuckDB without standing up a warehouse.
- 04 Push aggregates computed in DuckDB out to a CRM or business tools so analysis results reach operational systems.
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 Snowflake and keep DuckDB focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from DuckDB land in Snowflake as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between DuckDB 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.
| DuckDB objects | Snowflake objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Namespaces within a database used to organize tables in sync outputs. | Schemas Namespaces within a database used to organize synced tables. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Tables Columnar tables created via SQL; the destination for materialized sync data. | 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. | |
| Views SQL views used to shape or filter data for downstream consumers. | Views Modeled projections used as the source side of outbound syncs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| External files (Parquet/CSV/JSON) Files DuckDB queries in place without loading, common as a sync interchange format. | Tasks Scheduled SQL used to transform synced data after it lands. | External files (Parquet/CSV/JSON) is specific to DuckDB and Tasks to Snowflake — each maps to any object or custom field on the other side. | |
| Attached databases Additional database files or external systems attached into one session for cross-source queries. | VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. | Attached databases is specific to DuckDB and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side. | |
| Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. | Virtual Warehouses The compute a sync's queries run on, sized independently of storage. | Database files is specific to DuckDB and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side. |
How changes propagate between DuckDB 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.
DuckDB Snowflake Interval-based propagation
DetectionStacksync polls DuckDB for changes on an incremental schedule, reading only records changed since the previous pass. Polling or full re-reads.
DeliveryEach detected change is applied to Snowflake as a row-level write, with types converted between the two schemas.
Snowflake DuckDB 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 DuckDB as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- DuckDB: No API rate limits; throughput is bounded by local compute and I/O.
- Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with DuckDB ⇄ Snowflake
Connect DuckDB and Snowflake for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every DuckDB–Snowflake connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever DuckDB 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 DuckDB or Snowflake record.
Observability
Monitoring
Track your DuckDB ⇄ Snowflake sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between DuckDB and Snowflake.
How the DuckDB and Snowflake connectors work
DuckDB
- Integration surface
- In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
- Authentication
- None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
- Change detection
- Polling or full re-reads; no change feed or transaction log API
- Capabilities
- read · write
- Rate limits
- No API rate limits; throughput is bounded by local compute and I/O
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
How it works
How to connect DuckDB 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.
- 01
Connect your apps
Authenticate DuckDB 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.
DuckDB
connected
Snowflake
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the DuckDB 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.
objects · DuckDB ⇄ SnowflakeCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
DuckDB SnowflakeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
DuckDB and Snowflake integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between DuckDB and Snowflake: authenticate both systems, choose the objects to sync (such as DuckDB's Schemas and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed DuckDB and Snowflake connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom DuckDB–Snowflake integration in-house.
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Yes — Stacksync ships production-grade connectors for both DuckDB and Snowflake. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on DuckDB: Polling or full re-reads; no change feed or transaction log API. On Snowflake: Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Snowflake side: Schemas, Tables, Views, Materialized Views, plus custom fields where Snowflake exposes them. On the DuckDB side: Attached databases, Database files, Schemas, Tables. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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.
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:
Related integrations
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