Cloudera Data Platform vs Databricks

Cloudera has no free plan or trial listed. Its cloud rates are metered by CCU per hour, with Data Engineering from $0.07 per CCU-hour, Data Warehouse at $0.07, and AI Workbench at $0.20. Data Flow deployments and test sessions are $0.30 per CCU-hour; functions start at $0.10 per billable invocation. Monthly totals and most seat and usage limits are not published, so estimate from workload hours and ask about volume discounts where listed.

Databricks Community Edition is free, with a single-node cluster and limited micro-cluster and storage functionality. Premium pay-as-you-go is billed by the second, with Data Engineering from $0.15/DBU and Data Warehousing from $0.22/DBU. Genie includes 150 free monthly DBUs, then starts at $0.07/DBU. The 14-day trial excludes cloud-provider infrastructure costs. Enterprise pricing uses custom commitments; prices are not published.

Cloudera's homepage identifies Apache Iceberg and says the platform connects disparate data sources and moves data across sources and destinations. Its integration story centers on a hybrid data environment rather than a published catalog of connectors. The vendor doesn't publish API styles, SDK languages, or CLI details on its product pages, so validate those interfaces and documentation before estimating adapter work.

Databricks lists AWS, Azure, and GCP support, IDE integrations, and a partner ecosystem. Its platform includes Lakeflow for batch and streaming ETL, plus data warehousing, application development, and AI features. The homepage does not name specific IDEs, SDKs, API protocols, or CLI commands. Teams should confirm which services and language clients they need, and whether partner integrations cover their existing stack. For both platforms, map required data sources and deployment targets before treating a general integration claim as a supported production connector.

Cloudera says its platform runs in public clouds, data centers, and at the edge, and it identifies Apache Iceberg as the basis for its open data lakehouse. That supports hybrid deployment and an open table-format choice; it does not establish that the Cloudera platform itself is open source or specify its license. Databricks lists open-source technologies, but that does not establish a platform license either. Confirm licensing for the platform and every component you deploy.

Cloudera supports on-premises deployment. Databricks lists AWS, Azure, and GCP cloud options; confirm any self-hosting requirements directly. Neither vendor publishes data-export procedures on the vendor's site available. Ask about table and file formats, metadata, governance policies, job definitions, and model artifacts during a migration. Test the export path for your chosen services before committing.