Real-time
Two-way sync
Changes in Materialize or Snowflake instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Materialize to Snowflake integration — real-time, two-way sync
Keep Materialize 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.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Materialize and Snowflake
Keep tables consistent across Materialize 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 Materialize 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 computed view results back into a CRM or application database as derived fields.
- 02 Drive alerting and operational tooling from SUBSCRIBE change streams instead of scheduled queries.
- 03 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
- 04 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on
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 Materialize 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.
| Materialize objects | Snowflake objects | How this pairing syncs | |
|---|---|---|---|
| Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. | 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 Incrementally maintained query results that syncs read as continuously up-to-date datasets. | 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. | |
| Sinks Outbound connections that emit view changes to Kafka topics. | VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. | Sinks is specific to Materialize and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side. | |
| Indexes In-memory arrangements that make view reads fast for serving workloads. | Virtual Warehouses The compute a sync's queries run on, sized independently of storage. | Indexes is specific to Materialize and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side. | |
| Clusters Compute pools that isolate ingestion, view maintenance, and serving. | Databases Top-level containers that scope which data a sync can touch. | Clusters is specific to Materialize and Databases to Snowflake — each maps to any object or custom field on the other side. | |
| Connections & Secrets Stored credentials and endpoints used by sources and sinks. | Schemas Namespaces within a database used to organize synced tables. | Connections & Secrets is specific to Materialize and Schemas to Snowflake — each maps to any object or custom field on the other side. |
How changes propagate between Materialize 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.
Materialize Snowflake Sub-second propagation
DetectionChanges in Materialize are captured at the source via change data capture — no polling loop against its API. SUBSCRIBE queries stream row-level changes of any view or table to the client.
DeliveryEach detected change is applied to Snowflake as a row-level write, with types converted between the two schemas.
Snowflake Materialize 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 Materialize 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.
What ships with Materialize ⇄ Snowflake
Connect Materialize and Snowflake for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Materialize–Snowflake connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Materialize 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 Materialize or Snowflake record.
Observability
Monitoring
Track your Materialize ⇄ Snowflake sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Materialize and Snowflake.
How the Materialize and Snowflake connectors work
Materialize
- Integration surface
- PostgreSQL wire protocol (SQL)
- Authentication
- Database credentials (username/password; app passwords in the managed cloud service)
- Change detection
- SUBSCRIBE queries stream row-level changes of any view or table to the client
- Capabilities
- read · write · CDC
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 Materialize 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 Materialize 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.
Materialize
connected
Snowflake
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Materialize 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 · Materialize ⇄ 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.
Materialize SnowflakeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Materialize and Snowflake integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Materialize and Snowflake: authenticate both systems, choose the objects to sync (such as Materialize's Tables and Materialized Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Materialize: PostgreSQL wire protocol (SQL). Authentication: Database credentials (username/password; app passwords in the managed cloud service). Snowflake: 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. Stacksync manages authentication, retries, and rate limits on both sides.
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Materialize: It ingests CDC from Postgres and MySQL and streams from Kafka as first-class sources. Snowflake: External tables are not supported. Stacksync's field mapping accounts for these differences between Materialize and Snowflake without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Materialize and Snowflake records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Materialize and Snowflake connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Materialize–Snowflake integration in-house.
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Yes — Stacksync ships production-grade connectors for both Materialize and Snowflake. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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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