Real-time
Two-way sync
Changes in Materialize or OpenSearch instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Materialize to OpenSearch integration — real-time, two-way sync
Keep Materialize and OpenSearch 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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Why teams connect Materialize and OpenSearch
Connect OpenSearch 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 OpenSearch'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 OpenSearch where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in OpenSearch sync into Materialize in real time, and result tables in Materialize sync back into OpenSearch, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Sync operational CRM or ERP data into Materialize so real-time views stay current without batch loads.
- 02 Read computed view results back into a CRM or application database as derived fields.
- 03 Stream CRM records such as accounts, contacts, and tickets into OpenSearch to power internal search across customer data.
- 04 Sync product catalogs from an ERP or database into the indexes that back storefront search.
Common sync patterns
Serve warehouse results at database speed
Aggregates or model outputs computed in Materialize sync into OpenSearch, 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 OpenSearch focused on its operational workload.
What you can sync between Materialize and OpenSearch
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 | OpenSearch objects | How this pairing syncs | |
|---|---|---|---|
| Indexes In-memory arrangements that make view reads fast for serving workloads. | Indexes The core container; synced records land in indexes with defined mappings | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Schemas & Databases Namespaces that organize objects a sync targets. | Documents JSON records written via the index and bulk APIs and read via search queries | Schemas & Databases is specific to Materialize and Documents to OpenSearch — each maps to any object or custom field on the other side. | |
| Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. | Index aliases Stable names over rotating indexes, used for zero-downtime reindex during backfills | Tables is specific to Materialize and Index aliases to OpenSearch — 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. | Index templates Mapping and settings presets applied to new indexes a sync creates | Sources is specific to Materialize and Index templates to OpenSearch — each maps to any object or custom field on the other side. | |
| Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. | Ingest pipelines Server-side processors that transform documents as they are written | Materialized Views is specific to Materialize and Ingest pipelines to OpenSearch — each maps to any object or custom field on the other side. | |
| Sinks Outbound connections that emit view changes to Kafka topics. | Data streams Append-oriented time-series storage for logs and events pushed from source systems | Sinks is specific to Materialize and Data streams to OpenSearch — each maps to any object or custom field on the other side. |
How changes propagate between Materialize and OpenSearch
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 OpenSearch 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 written to OpenSearch through its API, with automatic retries and rate-limit backoff.
OpenSearch Materialize Interval-based propagation
DetectionStacksync polls OpenSearch for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.
DeliveryEach detected change is applied to Materialize as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- OpenSearch: Throughput bounded by cluster sizing rather than fixed API quotas.
What ships with Materialize ⇄ OpenSearch
Connect Materialize and OpenSearch for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Materialize–OpenSearch connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Materialize or OpenSearch 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 OpenSearch record.
Observability
Monitoring
Track your Materialize ⇄ OpenSearch 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 OpenSearch.
How the Materialize and OpenSearch 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
OpenSearch
- Integration surface
- REST API over HTTP(S) with JSON payloads
- Authentication
- basic authentication with the security plugin, or AWS IAM request signing on Amazon OpenSearch Service
- Change detection
- no native change feed; reads rely on queries with scroll or point-in-time polling
- Capabilities
- read · write
- Rate limits
- throughput bounded by cluster sizing rather than fixed API quotas
How it works
How to connect Materialize to OpenSearch — 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 OpenSearch 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
OpenSearch
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Materialize and OpenSearch 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 ⇄ OpenSearchCustomers 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 OpenSearchCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Materialize and OpenSearch integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Materialize and OpenSearch: authenticate both systems, choose the objects to sync (such as Materialize's Indexes and Schemas & Databases), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Common patterns for Materialize and OpenSearch: Serve warehouse results at database speed; Fresh analytics without loading windows; Offload heavy reads. Aggregates or model outputs computed in Materialize sync into OpenSearch, where whatever reads from that database gets them without querying the warehouse.
-
Materialize: PostgreSQL wire protocol (SQL). Authentication: Database credentials (username/password; app passwords in the managed cloud service). OpenSearch: REST API over HTTP(S) with JSON payloads. Authentication: Basic authentication with the security plugin, or AWS IAM request signing on Amazon OpenSearch Service. Stacksync manages authentication, retries, and rate limits on both sides.
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Materialize: Views are maintained incrementally as data arrives rather than recomputed at query time, which is what makes reads consistently fresh. OpenSearch: OpenSearch exposes no built-in change feed, so it usually serves as a sync destination with sources pushing documents in through the bulk API. Stacksync's field mapping accounts for these differences between Materialize and OpenSearch 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 OpenSearch 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 OpenSearch connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Materialize–OpenSearch integration in-house.
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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