Real-time
Two-way sync
Changes in Apache Pinot or Materialize instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Apache Pinot to Materialize integration — real-time, two-way sync
Keep Apache Pinot 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
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 Apache Pinot and Materialize
Keep tables consistent across Apache Pinot 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 Apache Pinot 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 Keep upsert-enabled real-time tables aligned with mutable operational records streamed from source systems.
- 02 Query per-account usage metrics from Pinot and sync them into CRM fields so sales sees product activity.
- 03 Bridge streaming sources and non-streaming destinations by materializing joins across both.
- 04 Sync operational CRM or ERP data into Materialize so real-time views stay current without batch loads.
Common sync patterns
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.
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.
What you can sync between Apache Pinot 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.
| Apache Pinot objects | Materialize objects | How this pairing syncs | |
|---|---|---|---|
| Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. | 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. | |
| Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. | Indexes In-memory arrangements that make view reads fast for serving workloads. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Offline Tables Batch-loaded tables merged with real-time data at query time. | Connections & Secrets Stored credentials and endpoints used by sources and sinks. | Offline Tables is specific to Apache Pinot and Connections & Secrets to Materialize — each maps to any object or custom field on the other side. | |
| Tenants Logical groupings that isolate workloads on shared clusters. | Schemas & Databases Namespaces that organize objects a sync targets. | Tenants is specific to Apache Pinot and Schemas & Databases to Materialize — each maps to any object or custom field on the other side. | |
| Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. | Sources Ingestion points (Kafka, Postgres CDC, MySQL CDC, webhook) that feed external data into Materialize. | Schemas is specific to Apache Pinot and Sources to Materialize — each maps to any object or custom field on the other side. | |
| Segments Immutable data files that batch ingestion uploads and the cluster serves. | Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. | Segments is specific to Apache Pinot and Materialized Views to Materialize — each maps to any object or custom field on the other side. |
How changes propagate between Apache Pinot 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.
Apache Pinot Materialize Interval-based propagation
DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.
DeliveryEach detected change is applied to Materialize as a row-level write, with types converted between the two schemas.
Materialize Apache Pinot 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 Apache Pinot as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
What ships with Apache Pinot ⇄ Materialize
Connect Apache Pinot and Materialize for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Pinot–Materialize connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Pinot 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 Apache Pinot or Materialize record.
Observability
Monitoring
Track your Apache Pinot ⇄ Materialize sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Apache Pinot and Materialize.
How the Apache Pinot and Materialize connectors work
Apache Pinot
- Integration surface
- REST API (SQL queries via the broker; administration via the controller); JDBC client available
- Authentication
- Deployment-dependent: HTTP basic authentication or token-based auth where enabled
- Change detection
- Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
- Capabilities
- read · write
- Rate limits
- No fixed API quotas; query throughput depends on broker and server sizing
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 Apache Pinot 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.
- 01
Connect your apps
Authenticate Apache Pinot 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.
Apache Pinot
connected
Materialize
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Pinot 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.
objects · Apache Pinot ⇄ MaterializeCustomers 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.
Apache Pinot MaterializeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Pinot and Materialize integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Pinot and Materialize: authenticate both systems, choose the objects to sync (such as Apache Pinot's Tables and Indexes), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Apache Pinot and Materialize: Shared datasets across teams; Consolidation after M&A; Migration without a big bang. Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
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Apache Pinot: REST API (SQL queries via the broker; administration via the controller); JDBC client available. Authentication: Deployment-dependent: HTTP basic authentication or token-based auth where enabled. Materialize: PostgreSQL wire protocol (SQL). Authentication: Database credentials (username/password; app passwords in the managed cloud service). Stacksync manages authentication, retries, and rate limits on both sides.
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Apache Pinot: Data is stored in immutable segments; batch writes happen by building and uploading segments rather than issuing row inserts. Materialize: Materialize speaks the PostgreSQL wire protocol, so standard Postgres drivers and tools connect without a custom client. Stacksync's field mapping accounts for these differences between Apache Pinot and Materialize 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 Apache Pinot and Materialize records are not retained after a sync operation.
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:
