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Data warehouse ⇄ Database

Materialize to PostgreSQL integration — real-time, two-way sync

Keep Materialize and PostgreSQL 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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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Materialize and PostgreSQL

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

Stacksync covers both directions with one connection. Tables or collections in PostgreSQL sync into Materialize in real time, and result tables in Materialize sync back into PostgreSQL, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Drive alerting and operational tooling from SUBSCRIBE change streams instead of scheduled queries.
  • 02 Maintain real-time inventory, usage, or account-health counters consumed by customer-facing applications.
  • 03 Feed reporting and BI from a continuously synced Postgres replica instead of scheduled ETL scripts
  • 04 Expose SaaS objects (CRM contacts, ERP invoices, support tickets) as Postgres tables that internal tools can query and join

Common sync patterns

Serve warehouse results at database speed

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

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

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

What you can sync between Materialize and PostgreSQL

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 PostgreSQL objects How this pairing syncs
Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems. 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 result sets synced outward on a refresh schedule. 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. Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution. Indexes is specific to Materialize and Primary and Unique Keys to PostgreSQL — each maps to any object or custom field on the other side.
Clusters Compute pools that isolate ingestion, view maintenance, and serving. JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata. Clusters is specific to Materialize and JSONB Columns to PostgreSQL — each maps to any object or custom field on the other side.
Connections & Secrets Stored credentials and endpoints used by sources and sinks. Sequences Generate surrogate keys for rows created by inbound syncs. Connections & Secrets is specific to Materialize and Sequences to PostgreSQL — each maps to any object or custom field on the other side.
Schemas & Databases Namespaces that organize objects a sync targets. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields. Schemas & Databases is specific to Materialize and Custom Types and Enums to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Materialize and PostgreSQL

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 PostgreSQL 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 PostgreSQL as a row-level write, with types converted between the two schemas.

PostgreSQL Materialize Sub-second propagation

DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.

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

Rate-limit considerations

  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with Materialize ⇄ PostgreSQL

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Materialize or PostgreSQL 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 PostgreSQL record.

Observability

Monitoring

Track your Materialize ⇄ PostgreSQL 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 PostgreSQL.

How the Materialize and PostgreSQL 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

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

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

    Choose tables

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

Materialize and PostgreSQL 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 476 integrations available for Materialize and PostgreSQL.

Popular · 6 of 476
All Materialize integrations All PostgreSQL integrations Browse all integrations
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