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Data warehouse

Amazon Redshift to Materialize integration — real-time, two-way sync

Keep Amazon Redshift and Materialize 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

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Migrated from MuleSoft
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Why teams connect Amazon Redshift and Materialize

Keep tables consistent across Amazon Redshift and Materialize, 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 Amazon Redshift and Materialize 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 Centralize CRM, ERP, and product data in Redshift so analysts join it with warehouse tables.
  • 02 Publish finance rollups computed in Redshift back to spreadsheets or operational tools.
  • 03 Maintain real-time inventory, usage, or account-health counters consumed by customer-facing applications.
  • 04 Bridge streaming sources and non-streaming destinations by materializing joins across both.

Common sync patterns

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.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

What you can sync between Amazon Redshift and Materialize

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.

Amazon Redshift objects Materialize objects How this pairing syncs
Tables Columnar tables used as sync destinations for SaaS and database data. Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Materialized Views Precomputed results that downstream syncs can read for performance. Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views SQL views readable as modeled sources for reverse syncs. Sinks Outbound connections that emit view changes to Kafka topics. Views is specific to Amazon Redshift and Sinks to Materialize — each maps to any object or custom field on the other side.
External Tables (Spectrum) S3-backed tables queryable through Redshift, readable in syncs. Indexes In-memory arrangements that make view reads fast for serving workloads. External Tables (Spectrum) is specific to Amazon Redshift and Indexes to Materialize — each maps to any object or custom field on the other side.
Stored Procedures SQL procedures sometimes invoked around load steps. Clusters Compute pools that isolate ingestion, view maintenance, and serving. Stored Procedures is specific to Amazon Redshift and Clusters to Materialize — each maps to any object or custom field on the other side.
Users and Groups Principals used to grant a sync connection scoped access. Connections & Secrets Stored credentials and endpoints used by sources and sinks. Users and Groups is specific to Amazon Redshift and Connections & Secrets to Materialize — each maps to any object or custom field on the other side.

How changes propagate between Amazon Redshift and Materialize

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.

Amazon Redshift Materialize Interval-based propagation

DetectionStacksync polls Amazon Redshift for changes on an incremental schedule, reading only records changed since the previous pass. Polling or query-based diffing.

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

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

Rate-limit considerations

  • Amazon Redshift: Bounded by cluster or serverless capacity and concurrency settings rather than API quotas.
What ships with Amazon Redshift ⇄ Materialize

Connect Amazon Redshift and Materialize for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Redshift or Materialize 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 Amazon Redshift or Materialize record.

Observability

Monitoring

Track your Amazon Redshift ⇄ Materialize sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Redshift and Materialize.

How the Amazon Redshift and Materialize connectors work

Amazon Redshift

Integration surface
SQL over JDBC/ODBC (PostgreSQL-derived protocol); Redshift Data API over HTTPS
Authentication
Database credentials or IAM-based authentication
Change detection
Polling or query-based diffing; Redshift does not expose a transaction log for external CDC consumers
Capabilities
read · write
Rate limits
Bounded by cluster or serverless capacity and concurrency settings rather than API quotas

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
How it works

How to connect Amazon Redshift to Materialize — 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 Amazon Redshift and Materialize 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
    Amazon Redshift connected
    Materialize connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Redshift and Materialize 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 · Amazon Redshift ⇄ Materialize
    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
    Amazon Redshift Materialize
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Redshift and Materialize 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 473 integrations available for Amazon Redshift and Materialize.

Popular · 5 of 473
All Amazon Redshift integrations All Materialize integrations Browse all integrations
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