Real-time
Two-way sync
Changes in Cloudera Data Platform or StarRocks instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Cloudera Data Platform to StarRocks integration — real-time, two-way sync
Keep Cloudera Data Platform and StarRocks 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 StarRocks
Keep tables consistent across Cloudera Data Platform and StarRocks, 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 StarRocks 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 Continuously apply upserts from operational databases into Primary Key tables to keep analytics current
- 04 Serve customer-facing analytics from SaaS data synced into one analytical store
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 Cloudera Data Platform and StarRocks
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 | StarRocks objects | How this pairing syncs | |
|---|---|---|---|
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Databases Top-level namespaces addressed exactly as in MySQL clients. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Views SQL views that can present curated, sync-ready projections of raw lake data. | Views Logical views for shaping analytical reads. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | Partitions Time or range partitions that scope loads and retention. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Impala tables The same metastore tables served through Impala for lower-latency SQL reads. | Materialized views Automatically maintained rollups used to accelerate queries on synced data. | Impala tables is specific to Cloudera Data Platform and Materialized views to StarRocks — 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. | Columns Columnar storage with types mapped from source systems during sync. | Kudu tables is specific to Cloudera Data Platform and Columns to StarRocks — 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. | Tables Defined with a table model (Primary Key, Unique Key, Aggregate, Duplicate Key) that determines update behavior. | Iceberg tables is specific to Cloudera Data Platform and Tables to StarRocks — each maps to any object or custom field on the other side. |
How changes propagate between Cloudera Data Platform and StarRocks
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 StarRocks 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 StarRocks as a row-level write, with types converted between the two schemas.
StarRocks Cloudera Data Platform Interval-based propagation
DetectionStacksync polls StarRocks for changes on an incremental schedule, reading only records changed since the previous pass. Query-based polling when reading.
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.
- StarRocks: Ingestion throughput is bounded by cluster resources rather than API quotas.
What ships with Cloudera Data Platform ⇄ StarRocks
Connect Cloudera Data Platform and StarRocks for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–StarRocks connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Cloudera Data Platform or StarRocks 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 StarRocks record.
Observability
Monitoring
Track your Cloudera Data Platform ⇄ StarRocks 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 StarRocks.
How the Cloudera Data Platform and StarRocks 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
StarRocks
- Integration surface
- MySQL wire protocol for SQL; HTTP-based Stream Load API for ingestion
- Authentication
- Database credentials (MySQL-compatible username/password)
- Change detection
- Query-based polling when reading; StarRocks is most often the destination side of a sync
- Capabilities
- read · write
- Rate limits
- Ingestion throughput is bounded by cluster resources rather than API quotas
How it works
How to connect Cloudera Data Platform to StarRocks — 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 StarRocks 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
StarRocks
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Cloudera Data Platform and StarRocks 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 ⇄ StarRocksCustomers 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 StarRocksCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Cloudera Data Platform and StarRocks integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Cloudera Data Platform and StarRocks: authenticate both systems, choose the objects to sync (such as Cloudera Data Platform's Databases and Views), 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 Cloudera Data Platform and StarRocks: Serve tools that only connect to one platform; Shared datasets across teams; Consolidation after M&A. Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
-
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. StarRocks: MySQL wire protocol for SQL; HTTP-based Stream Load API for ingestion. Authentication: Database credentials (MySQL-compatible username/password). 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. StarRocks: Stream Load ingests data over HTTP in batches, giving pipelines a load path separate from SQL INSERT. Stacksync's field mapping accounts for these differences between Cloudera Data Platform and StarRocks 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 StarRocks 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:
Related integrations
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