Skip to content
Data warehouse ⇄ Database

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

Keep Materialize 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.

  • 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 SQL Server

Connect SQL Server and Materialize 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 Materialize, 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 Materialize in real time, and result tables in Materialize sync back into SQL Server, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Maintain real-time inventory, usage, or account-health counters consumed by customer-facing applications.
  • 02 Bridge streaming sources and non-streaming destinations by materializing joins across both.
  • 03 Consolidate branch or plant databases into a single operational SQL Server hub
  • 04 Bi-directional sync between SQL Server rows and CRM objects so .NET line-of-business apps and sales tools share one dataset

Common sync patterns

Offload heavy reads

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

Operational data in the warehouse, minus the pipeline

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

Serve warehouse results at database speed

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

What you can sync between Materialize 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.

Materialize objects SQL Server objects How this pairing syncs
Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. 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.
Indexes In-memory arrangements that make view reads fast for serving workloads. Schemas Namespaces (dbo and custom) used to organize synced tables. Indexes is specific to Materialize and Schemas to SQL Server — each maps to any object or custom field on the other side.
Clusters Compute pools that isolate ingestion, view maintenance, and serving. Views Read-side projections used as outbound sync sources. Clusters is specific to Materialize and Views to SQL Server — each maps to any object or custom field on the other side.
Connections & Secrets Stored credentials and endpoints used by sources and sinks. Columns Field-level mapping targets with T-SQL types. Connections & Secrets is specific to Materialize and Columns to SQL Server — each maps to any object or custom field on the other side.
Schemas & Databases Namespaces that organize objects a sync targets. Primary and Unique Keys Match keys for idempotent upserts and conflict handling. Schemas & Databases is specific to Materialize and Primary and Unique Keys to SQL Server — each maps to any object or custom field on the other side.
Sources Ingestion points (Kafka, Postgres CDC, MySQL CDC, webhook) that feed external data into Materialize. CDC Change Tables System-populated tables holding captured inserts, updates, and deletes for consumers. Sources is specific to Materialize and CDC Change Tables to SQL Server — each maps to any object or custom field on the other side.

How changes propagate between Materialize 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.

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

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

Rate-limit considerations

  • SQL Server: No API rate limits; throughput depends on instance resources, licensing tier, and connection limits.
What ships with Materialize ⇄ SQL Server

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Materialize 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 Materialize or SQL Server record.

Observability

Monitoring

Track your Materialize ⇄ 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 Materialize and SQL Server.

How the Materialize and SQL Server 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

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 Materialize 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 Materialize 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
    Materialize connected
    SQL Server connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Materialize 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 470 integrations available for Materialize and SQL Server.

Popular · 6 of 470
All Materialize integrations All SQL Server integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own