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Data warehouse ⇄ Database

Snowflake to SQL Server integration — real-time, two-way sync

Keep Snowflake and SQL Server 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 Snowflake and SQL Server

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

Stacksync covers both directions with one connection. Tables or collections in SQL Server sync into Snowflake in real time, and result tables in Snowflake sync back into SQL Server, 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 Bi-directional sync between SQL Server rows and CRM objects so .NET line-of-business apps and sales tools share one dataset
  • 04 Expose SaaS records as SQL Server tables that existing SSRS reports and internal apps can query

Common sync patterns

Offload heavy reads

Point analytical queries at the synced copy in Snowflake and keep SQL Server focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from SQL Server land in Snowflake as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in Snowflake sync into SQL Server, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Snowflake and SQL Server

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.

Snowflake objects SQL Server objects How this pairing syncs
Databases Top-level containers that scope which data a sync can touch. Databases Instance-level databases that scope a sync's reads and writes. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Schemas Namespaces within a database used to organize synced tables. Schemas Namespaces (dbo and custom) used to organize synced tables. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. Custom fields on either side are included in the mapping.
Tables The main landing and activation target for synced records. Tables The primary sync target; rows map to records in connected systems. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views Modeled projections used as the source side of outbound syncs. Views Read-side projections used as outbound sync sources. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Streams Row-level change records on a table, consumed to process deltas instead of full scans. Columns Field-level mapping targets with T-SQL types. Streams is specific to Snowflake and Columns to SQL Server — each maps to any object or custom field on the other side.
Stages File staging areas used for bulk loads into synced tables. Primary and Unique Keys Match keys for idempotent upserts and conflict handling. Stages is specific to Snowflake and Primary and Unique Keys to SQL Server — each maps to any object or custom field on the other side.

How changes propagate between Snowflake and SQL Server

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.

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

SQL Server Snowflake Sub-second propagation

DetectionChanges in SQL Server are captured at the source via change data capture — no polling loop against its API. SQL Server Native Change Data Capture (CDC).

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

Rate-limit considerations

  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
  • SQL Server: No API rate limits; throughput depends on instance resources, licensing tier, and connection limits.
What ships with Snowflake ⇄ SQL Server

Connect Snowflake and SQL Server for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Snowflake or SQL Server 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 Snowflake or SQL Server record.

Observability

Monitoring

Track your Snowflake ⇄ SQL Server sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Snowflake and SQL Server.

How the Snowflake and SQL Server connectors work

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

SQL Server

Integration surface
SQL over the TDS wire protocol (Tabular Data Stream), via ODBC/JDBC/ADO.NET drivers
Authentication
Database credentials entered as a connection string or as parameters (host/user/password) in the Create New Sync page
Change detection
SQL Server Native Change Data Capture (CDC); a DBA runs a one-time setup script with sysadmin privileges to enable CDC and create Stacksync wrapper procedures
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput depends on instance resources, licensing tier, and connection limits
SQL Server setup guide
How it works

How to connect Snowflake to SQL Server — 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 Snowflake and SQL Server 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
    Snowflake connected
    SQL Server connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Snowflake and SQL Server 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 · Snowflake ⇄ SQL Server
    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
    Snowflake SQL Server
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Snowflake and SQL Server 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
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:

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 580 integrations available for Snowflake and SQL Server.

Popular · 8 of 580
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