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Data warehouse

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

Keep Cloudera Data Platform and Databricks 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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Migrated from MuleSoft
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Migrated from Fivetran
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Why teams connect Cloudera Data Platform and Databricks

Keep tables consistent across Cloudera Data Platform and Databricks, 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 Databricks 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 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

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 Databricks

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 Databricks objects How this pairing syncs
Views SQL views that can present curated, sync-ready projections of raw lake data. Views Curated read-only projections used as sync sources for downstream tools. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Iceberg tables is specific to Cloudera Data Platform and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Partitions is specific to Cloudera Data Platform and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. Volumes Unity Catalog file storage used for staging bulk loads. Object store / HDFS files is specific to Cloudera Data Platform and Volumes to Databricks — 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. SQL Warehouses The compute endpoint a sync connects to for query execution. Databases is specific to Cloudera Data Platform and SQL Warehouses to Databricks — 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. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Hive tables is specific to Cloudera Data Platform and Change Data Feed to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Cloudera Data Platform and Databricks

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 Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Databricks Cloudera Data Platform Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Cloudera Data Platform ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Cloudera Data Platform or Databricks 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 Databricks record.

Observability

Monitoring

Track your Cloudera Data Platform ⇄ Databricks 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 Databricks.

How the Cloudera Data Platform and Databricks 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

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
How it works

How to connect Cloudera Data Platform to Databricks — 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 Cloudera Data Platform and Databricks 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
    Cloudera Data Platform connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Cloudera Data Platform and Databricks 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 · Cloudera Data Platform ⇄ Databricks
    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
    Cloudera Data Platform Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Cloudera Data Platform and Databricks 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
CSA STAR
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 480 integrations available for Cloudera Data Platform and Databricks.

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