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Data warehouse · Two-way sync platform

MotherDuck and Snowflake integration — two-way sync

Keep supported MotherDuck and Snowflake records aligned with two-way sync. Give each team access to current data while controlling which system can update each field.

  • Field mappings and sync direction under your control
  • Inspect and resolve record errors in one dashboard
Two-way sync for supported objects
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MotherDuck
Snowflake
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Example workflow

Plan table mappings between MotherDuck and Snowflake

Validate the selected objects and operations even where connector-level direction is documented.

Starting eventA selected source table needs an operational replica, a reporting projection, or a migration copy.

  1. Choose actual tables in MotherDuck and Snowflake and compare their schemas and record types.

  2. Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.

  3. Plan two-way sync around connector support and your update rules; use a source-to-destination copy for read-only records. Keep source views, aggregate outputs, and writable base tables distinct.

What to verifyCounts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.

Review records and field ownership

Mapping essentials

Records and field ownership

Match records by stable IDs and assign an owner for each field. The record notes identify the coverage to check.

Download the mapping worksheet

CSV · No email required

Choose the tables you need in MotherDuck and Snowflake, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.

Why teams connect MotherDuck and Snowflake

Put your MotherDuck and Snowflake data to work

Start with the work your team needs to complete: keep records up to date, make operational data available for reporting, or move data to a new system. Choose one MotherDuck and Snowflake workflow to test, define what success looks like, and decide who handles failed updates.

  • Land CRM and operational database records in MotherDuck so a small team gets warehouse-style analytics without cluster management

  • Sync modeled MotherDuck tables outward to operational tools for activation

  • Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL

  • Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on

What records can you sync?

Start with the records your workflow needs. Check each system’s read and write requirements before mapping fields.

MotherDuck

Read and write support varies by record

Record types covered in the setup guide

Record typesCoverage and requirements
  • Tables (confirm coverage)
See connector requirements. Confirm field permissions and sync direction.

Read the MotherDuck connector guide

Snowflake

Read and write support varies by record

Record types covered in the setup guide

Record typesCoverage and requirements
  • Standard tables (including Permanent, Transient and Temporary tables)
✅ Supported. Confirm field permissions and sync direction.
  • Views (including materialized and non-materialized views)
🕘 Coming soon. Confirm field permissions and sync direction.
  • External tables
❌ Not supported. Confirm field permissions and sync direction.

Read the Snowflake connector guide

Connection essentials

Review how each system connects, detects changes, and permits access to your records.

View setup requirements and limits
Connection requirementMotherDuckSnowflake
Integration interfaceSQL through DuckDB clients and drivers using a MotherDuck (md:) connectionSQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
AuthenticationAccess token created in MotherDuckDedicated Snowflake service user + role with RSA key-pair authentication
Change detectionThe saved Stacksync guide does not specify the change-detection mechanism.The saved Stacksync guide does not specify the change-detection mechanism.
Read accessAvailable for supported recordsAvailable for supported records
Write accessAvailable for supported recordsAvailable for supported records

Enterprise controls

Security and control for your integrations

Explore security controls

Compliance and data transfers

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

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:

Record-level recovery

Inspect sync errors and use retry and revert controls to resolve failed updates.

Read the recovery guide

Implementation

Technical reference

Review setup, record relationships, testing, and recovery for your implementation.

Authentication, permissions and API limits

Connection requirements and limits

MotherDuck
Integration interface
SQL through DuckDB clients and drivers using a MotherDuck (md:) connection
Authentication
Access token created in MotherDuck (Settings > General > Create Token), pasted into Stacksync; database name and schema configurable if not using defaults
Change detection
The saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.
Read access
Available for supported records
Write access
Available for supported records
Setup requirements
  • Generate a MotherDuck access token in Settings > General and enter it in the Stacksync connection form.
  • Specify the intended database and schema and confirm read/write coverage for the selected tables.
Limitations to check
  • Two-way sync is documented at connector level. Confirm table eligibility, writable columns, and the change-detection interval during setup.
Technical documentation

Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.

MotherDuck setup guide
Snowflake
Integration interface
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
The saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.
Read access
Available for supported records
Write access
Available for supported records
Setup requirements
  • Have an administrator run the documented setup script for a dedicated service user and role with RSA key-pair authentication.
  • Specify the correct warehouse, database, schema, and account identifier. Verify table grants for the service role.
  • Existing tables need a non-null primary key with a UUID_STRING() default, as specified in the connector guide.
Limitations to check
  • The saved support matrix excludes external tables and marks materialized and ordinary views as not yet supported.
  • Geography and geometry fields are excluded by the saved connector guide.
Technical documentation

Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.

Snowflake setup guide

Prepare MotherDuck and Snowflake access

Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.

MotherDuck setup checklist
  • Generate a MotherDuck access token in Settings > General and enter it in the Stacksync connection form.
  • Specify the intended database and schema and confirm read/write coverage for the selected tables.
Snowflake setup checklist
  • Have an administrator run the documented setup script for a dedicated service user and role with RSA key-pair authentication.
  • Specify the correct warehouse, database, schema, and account identifier. Verify table grants for the service role.
  • Existing tables need a non-null primary key with a UUID_STRING() default, as specified in the connector guide.

Setup guides: Authorize Snowflake

Prepare to go live

Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.

Use the MotherDuck and Snowflake planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

See Stacksync in action · Review current pricing

Record identity and field ownership

Keep each record tied to its source ID. Use the references below to choose field owners and preserve relationships between records.

Download the mapping worksheet · CSV, no email required

Choose the tables you need in MotherDuck and Snowflake, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.

Compare integration approaches

Choose your tables and update rules before the integration method. A live application copy, a reporting table, and a one-time migration need different handling for updates, history, deletes, and reconciliation.

Stacksync managed sync

Best fit
Two-way sync for supported objects. Choose the records, writable fields, and permissions for your workflow.
Operating responsibility
Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
Before you choose
Provide your table or object schemas and one example update; confirm the supported keys, fields, and direction.

Native vendor integration

Best fit
A vendor-built integration may fit if it supports your MotherDuck and Snowflake record types.
Operating responsibility
Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
Before you choose
First check whether either vendor offers this integration. If available, verify your record types, update direction, account tier, and related-record handling.

Custom API or workflow

Best fit
Consider when MotherDuck and Snowflake need a transformation, approval, or action outside a direct record sync.
Operating responsibility
Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
Before you choose
Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.

File or scheduled snapshot

Best fit
Consider for a one-time MotherDuck / Snowflake migration or a reporting need with an explicit freshness window.
Operating responsibility
Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
Before you choose
Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.

Workflow scenarios and expected results

Plan table mappings between MotherDuck and Snowflake

Validate the selected objects and operations even where connector-level direction is documented.

Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.

  1. Choose actual tables in MotherDuck and Snowflake and compare their schemas and record types.
  2. Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
  3. Plan two-way sync around connector support and your update rules; use a source-to-destination copy for read-only records. Keep source views, aggregate outputs, and writable base tables distinct.

Expected result: Counts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.

If it fails: Compare current source state, key mapping, and destination constraints before retrying. Reconcile the backlog after any schema or permission change.

Initial load and acceptance testing

Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.

Direction and permissions

Test case

Check that each connected account can read and write the chosen objects. Exercise both directions with a test record before enabling production changes.

Expected result

Only an approved, supported direction and permitted fields are written.

Schema, keys, and reconciliation

Test case

Use an actual MotherDuck and Snowflake table. Compare key uniqueness, nulls, decimal precision, timezone conversions, and counts within identical filters.

Expected result

The process meets its agreed freshness target and reconciliation has no unexplained differences.

Failed updates, retries and recovery

Start with the failed record and the destination error, then inspect the source value, field requirements, and access.

A record type or update is unavailable

Investigate

Check the MotherDuck and Snowflake connector guides, account permissions, and any operations marked On Request.

Next action

Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.

Source and destination disagree after a retry

Investigate

Compare current source values, destination validation, identity mappings, and any side effects already completed.

Next action

Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.

Read the Stacksync issues dashboard guide for retry and revert behavior.

Change detection and update delivery

How updates move between MotherDuck and Snowflake

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

MotherDuck Snowflake Timing depends on the connected systems

Detect changesThe saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.

Apply updatesSelected writable fields update in Snowflake. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.

Snowflake MotherDuck Timing depends on the connected systems

Detect changesThe saved Stacksync guide does not specify the change-detection mechanism. Confirm it for the selected objects.

Apply updatesSelected writable fields update in MotherDuck. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.

Update timing and record limits
  • Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
  • Two-way sync is documented at connector level. Confirm table eligibility, writable columns, and the change-detection interval during setup.
FAQ

MotherDuck and Snowflake integration FAQ

Further reading

Guides and resources

Next step

See your workflow in Stacksync

Walk through your MotherDuck and Snowflake records, field mappings, and requirements with an integration engineer.

See Stacksync in action Explore plans and pricing