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Data warehouse

Amazon Redshift to Cloudera Data Platform integration — real-time, two-way sync

Keep Amazon Redshift and Cloudera Data Platform 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 Amazon Redshift and Cloudera Data Platform

Keep tables consistent across Amazon Redshift and Cloudera Data Platform, 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 Amazon Redshift and Cloudera Data Platform 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 finance rollups computed in Redshift back to spreadsheets or operational tools.
  • 02 Feed customer 360 tables built in Redshift to support and success platforms.
  • 03 Sync curated CDP tables into an operational Postgres so applications query a low-latency copy instead of hitting the cluster.
  • 04 Publish CRM or ERP records into CDP so enterprise analytics runs alongside existing data lake workloads.

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 Amazon Redshift and Cloudera Data Platform

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.

Amazon Redshift objects Cloudera Data Platform objects How this pairing syncs
Databases Top-level containers within a cluster or serverless workgroup. Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views SQL views readable as modeled sources for reverse syncs. Views SQL views that can present curated, sync-ready projections of raw lake data. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Stored Procedures SQL procedures sometimes invoked around load steps. Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. Stored Procedures is specific to Amazon Redshift and Kudu tables to Cloudera Data Platform — each maps to any object or custom field on the other side.
Users and Groups Principals used to grant a sync connection scoped access. Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. Users and Groups is specific to Amazon Redshift and Iceberg tables to Cloudera Data Platform — each maps to any object or custom field on the other side.
Schemas Namespaces used to organize synced tables and control grants. Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. Schemas is specific to Amazon Redshift and Partitions to Cloudera Data Platform — each maps to any object or custom field on the other side.
Tables Columnar tables used as sync destinations for SaaS and database data. Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. Tables is specific to Amazon Redshift and Object store / HDFS files to Cloudera Data Platform — each maps to any object or custom field on the other side.

How changes propagate between Amazon Redshift and Cloudera Data Platform

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.

Amazon Redshift Cloudera Data Platform Interval-based propagation

DetectionStacksync polls Amazon Redshift for changes on an incremental schedule, reading only records changed since the previous pass. Polling or query-based diffing.

DeliveryEach detected change is applied to Cloudera Data Platform as a row-level write, with types converted between the two schemas.

Cloudera Data Platform Amazon Redshift 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 Amazon Redshift as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon Redshift: Bounded by cluster or serverless capacity and concurrency settings rather than API quotas.
  • Cloudera Data Platform: Constrained by cluster capacity and admission control rather than API rate limits.
What ships with Amazon Redshift ⇄ Cloudera Data Platform

Connect Amazon Redshift and Cloudera Data Platform for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Redshift–Cloudera Data Platform connection.

Real-time

Two-way sync

Changes in Amazon Redshift or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Redshift or Cloudera Data Platform 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 Amazon Redshift or Cloudera Data Platform record.

Observability

Monitoring

Track your Amazon Redshift ⇄ Cloudera Data Platform sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Redshift and Cloudera Data Platform.

How the Amazon Redshift and Cloudera Data Platform connectors work

Amazon Redshift

Integration surface
SQL over JDBC/ODBC (PostgreSQL-derived protocol); Redshift Data API over HTTPS
Authentication
Database credentials or IAM-based authentication
Change detection
Polling or query-based diffing; Redshift does not expose a transaction log for external CDC consumers
Capabilities
read · write
Rate limits
Bounded by cluster or serverless capacity and concurrency settings rather than API quotas

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
How it works

How to connect Amazon Redshift to Cloudera Data Platform — 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.

  1. 01

    Connect your apps

    Authenticate Amazon Redshift and Cloudera Data Platform with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon Redshift connected
    Cloudera Data Platform connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Redshift and Cloudera Data Platform 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon Redshift ⇄ Cloudera Data Platform
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Amazon Redshift Cloudera Data Platform
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Redshift and Cloudera Data Platform integration FAQ

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.

Learn more about security
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 472 integrations available for Amazon Redshift and Cloudera Data Platform.

Popular · 5 of 472
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