Real-time
Two-way sync
Changes in Cloudera Data Platform or Materialize instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Cloudera Data Platform to Materialize integration — real-time, two-way sync
Keep Cloudera Data Platform 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 Cloudera Data Platform and Materialize
Keep tables consistent across Cloudera Data Platform 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 Cloudera Data Platform 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 Publish CRM or ERP records into CDP so enterprise analytics runs alongside existing data lake workloads.
- 02 Consolidate tables from on-prem and cloud CDP environments into a single cloud warehouse target.
- 03 Read computed view results back into a CRM or application database as derived fields.
- 04 Drive alerting and operational tooling from SUBSCRIBE change streams instead of scheduled queries.
Common sync patterns
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.
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.
What you can sync between Cloudera Data Platform 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.
| Cloudera Data Platform objects | Materialize objects | How this pairing syncs | |
|---|---|---|---|
| Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | Schemas & Databases Namespaces that organize objects a sync targets. | Object store / HDFS files is specific to Cloudera Data Platform and Schemas & Databases to Materialize — each maps to any object or custom field on the other side. | |
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. | Databases is specific to Cloudera Data Platform and Tables to Materialize — each maps to any object or custom field on the other side. | |
| Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | Sources Ingestion points (Kafka, Postgres CDC, MySQL CDC, webhook) that feed external data into Materialize. | Hive tables is specific to Cloudera Data Platform and Sources to Materialize — each maps to any object or custom field on the other side. | |
| Impala tables The same metastore tables served through Impala for lower-latency SQL reads. | Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. | Impala tables is specific to Cloudera Data Platform and Materialized Views to Materialize — each maps to any object or custom field on the other side. | |
| Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. | Sinks Outbound connections that emit view changes to Kafka topics. | Kudu tables is specific to Cloudera Data Platform and Sinks to Materialize — each maps to any object or custom field on the other side. | |
| Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. | Indexes In-memory arrangements that make view reads fast for serving workloads. | Iceberg tables is specific to Cloudera Data Platform and Indexes to Materialize — each maps to any object or custom field on the other side. |
How changes propagate between Cloudera Data Platform 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.
Cloudera Data Platform Materialize Interval-based propagation
DetectionStacksync polls Cloudera Data Platform for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL on timestamp or partition columns.
DeliveryEach detected change is applied to Materialize as a row-level write, with types converted between the two schemas.
Materialize Cloudera Data Platform 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 Cloudera Data Platform as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Cloudera Data Platform: Constrained by cluster capacity and admission control rather than API rate limits.
What ships with Cloudera Data Platform ⇄ Materialize
Connect Cloudera Data Platform and Materialize for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–Materialize connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Cloudera Data Platform 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 Cloudera Data Platform or Materialize record.
Observability
Monitoring
Track your Cloudera Data Platform ⇄ Materialize sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Cloudera Data Platform and Materialize.
How the Cloudera Data Platform and Materialize connectors work
Cloudera Data Platform
- Integration surface
- JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs
- Authentication
- Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway
- Change detection
- Polling via SQL on timestamp or partition columns; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Constrained by cluster capacity and admission control rather than API rate limits
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 Cloudera Data Platform 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 Cloudera Data Platform 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.
Cloudera Data Platform
connected
Materialize
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Cloudera Data Platform 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 · Cloudera Data Platform ⇄ 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.
Cloudera Data Platform MaterializeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Cloudera Data Platform and Materialize integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Cloudera Data Platform and Materialize: authenticate both systems, choose the objects to sync (such as Cloudera Data Platform's Object store / HDFS files and Databases), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Common patterns for Cloudera Data Platform and Materialize: Consolidation after M&A; Migration without a big bang; Serve tools that only connect to one platform. Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
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Cloudera Data Platform: JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs. Authentication: Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway. 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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Cloudera Data Platform: Tables can live in multiple storage engines with different update semantics: Kudu and Iceberg tables support row-level updates, while classic Hive tables are append-oriented. Materialize: It ingests CDC from Postgres and MySQL and streams from Kafka as first-class sources. Stacksync's field mapping accounts for these differences between Cloudera Data Platform 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 Cloudera Data Platform and Materialize 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 Cloudera Data Platform and Materialize connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Cloudera Data Platform–Materialize 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
Every pair below is a real-time, two-way sync. Search all 369 integrations available for Cloudera Data Platform and Materialize.
Popular · 3 of 369
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Cloudera Data Platform ⇄ Starburst Enterprise -
Materialize ⇄ Azure Synapse Analytics -
Materialize ⇄ ClickHouse
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