Real-time
Two-way sync
Changes in Cloudera Data Platform or Snowflake instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Cloudera Data Platform to Snowflake integration — real-time, two-way sync
Keep Cloudera Data Platform and Snowflake 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
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Cloudera Data Platform and Snowflake
Keep tables consistent across Cloudera Data Platform and Snowflake, 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 Snowflake 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 Consolidate tables from on-prem and cloud CDP environments into a single cloud warehouse target.
- 02 Sync curated CDP tables into an operational Postgres so applications query a low-latency copy instead of hitting the cluster.
- 03 Keep a customer 360 table aligned with its source systems in both directions instead of one-way reverse ETL
- 04 Push product usage aggregates from Snowflake into sales and success tools for account prioritization
Common sync patterns
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.
Shared datasets across teams
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
What you can sync between Cloudera Data Platform and Snowflake
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 | Snowflake objects | How this pairing syncs | |
|---|---|---|---|
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Databases Top-level containers that scope which data a sync can touch. | 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 Modeled projections used as the source side of outbound syncs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | Tasks Scheduled SQL used to transform synced data after it lands. | Hive tables is specific to Cloudera Data Platform and Tasks to Snowflake — 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. | VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. | Impala tables is specific to Cloudera Data Platform and VARIANT Columns to Snowflake — 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. | Virtual Warehouses The compute a sync's queries run on, sized independently of storage. | Kudu tables is specific to Cloudera Data Platform and Virtual Warehouses to Snowflake — 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. | Schemas Namespaces within a database used to organize synced tables. | Iceberg tables is specific to Cloudera Data Platform and Schemas to Snowflake — each maps to any object or custom field on the other side. |
How changes propagate between Cloudera Data Platform and Snowflake
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 Snowflake 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 Snowflake as a row-level write, with types converted between the two schemas.
Snowflake Cloudera Data Platform Sub-second propagation
DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.
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.
- Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Cloudera Data Platform ⇄ Snowflake
Connect Cloudera Data Platform and Snowflake for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–Snowflake connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Cloudera Data Platform or Snowflake 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 Snowflake record.
Observability
Monitoring
Track your Cloudera Data Platform ⇄ Snowflake 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 Snowflake.
How the Cloudera Data Platform and Snowflake 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
Snowflake
- Integration surface
- SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
- Authentication
- Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
- Change detection
- Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
- Capabilities
- read · write · CDC
- Rate limits
- No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
How it works
How to connect Cloudera Data Platform to Snowflake — 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 Snowflake 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
Snowflake
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Cloudera Data Platform and Snowflake 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 ⇄ SnowflakeCustomers 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 SnowflakeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Cloudera Data Platform and Snowflake integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Cloudera Data Platform and Snowflake: 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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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 Snowflake 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 Snowflake connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Cloudera Data Platform–Snowflake integration in-house.
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Yes — Stacksync ships production-grade connectors for both Cloudera Data Platform and Snowflake. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Cloudera Data Platform: Polling via SQL on timestamp or partition columns; no consumer-facing change feed. On Snowflake: Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Cloudera Data Platform side: Iceberg tables, Views, Partitions, Object store / HDFS files, plus custom fields where Cloudera Data Platform exposes them. On the Snowflake side: VARIANT Columns, Virtual Warehouses, Databases, Schemas. Stacksync auto-detects both schemas and converts types between the two systems.
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 477 integrations available for Cloudera Data Platform and Snowflake.
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