Lakehouse platforms & table formats · Cloudera, Inc.

Cloudera Data Platform

Enterprise data platform unifying data engineering, warehousing, ML, and streaming across hybrid and multi-cloud environments.

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Cloudera Data Platform (CDP) is an enterprise lakehouse suite descended from the Hadoop-era Cloudera and Hortonworks stacks, now rebuilt around open table formats and containerized services. It packages Cloudera Data Warehouse, Data Engineering (Spark), Machine Learning, and Data Flow (based on Apache NiFi and Kafka) under shared security, governance, and metadata services (SDX), and can run on-premises, on any major public cloud, or in a hybrid mix of both. CDP is aimed at large, often regulated enterprises with existing Hadoop-ecosystem investments (HDFS, Hive, HBase, Kafka, Spark) who need a path to a governed lakehouse without a full platform replacement. It differs from cloud-native lakehouses like Databricks in emphasizing hybrid and on-premises deployment alongside cloud.

At a glance

Vendor Cloudera, Inc.
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Large regulated enterprises with existing Hadoop-ecosystem investments needing hybrid or on-premises lakehouse deployment.

Pricing

Consumption-based licensing (Cloudera Consumption Units) across compute and services; public pricing figures are not published and require contacting Cloudera sales.

Pricing has not been verified yet — see the vendor's site.

Features

  • Unified data warehouse, data engineering, ML, and streaming services
  • Shared governance and security via SDX (Shared Data Experience)
  • Hybrid and multi-cloud deployment, including on-premises
  • Built on open-source Hadoop-ecosystem components (Spark, Hive, Kafka, NiFi)
  • Cloudera Machine Learning workbenches for data science teams
  • Fine-grained data lineage and cataloging across workloads
  • Support for open table formats including Apache Iceberg

Integrations

Profile last reviewed September 21, 2026

Alternatives

Databricks Managed lakehouse platform, built by Apache Spark's creators, combining Spark-based compute with governance and ML tooling. Dremio SQL query and semantic layer engine for querying data lakes directly, built around Apache Iceberg and Apache Arrow. Onehouse Managed lakehouse service built by Apache Hudi's original creators, unifying ingestion and open table formats.

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Terms to know

Data lakehouse A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Apache Iceberg An open table format adding ACID transactions, schema evolution, and time travel to files stored in a data lake. Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions.

Related guides

How to choose a lakehouse platform Choosing a lakehouse means two decisions, not one — which open table format your data sits in, and which platform manages and queries it.