CIOPages
For vendorsCorrections to your listing are free
Fix an error, free List a company, free Order a Spotlight listingAbout Spotlight listings
DirectoryData & AnalyticsData Warehouse & LakehouseCloudera

Cloudera

About Cloudera

Cloudera Anywhere Cloud is a modular hybrid data and AI platform. The company describes itself as a data and AI platform company. Its offering spans areas described as Cloud Anywhere, AI Anywhere, Data Anywhere, Unified Data Fabric, and Data in Motion. Cloudera AI is described as a platform for enterprise AI from research to production. Cloudera Data Warehouse analyzes large amounts of data for many concurrent users.

Products

  • Cloudera AI: Provides a platform for enterprise AI from research to production.
  • Cloudera AI Inference service: Deploys and scales private AI applications, agents, and assistants.
  • AI Studios: Provides private generative AI and agentic workflows with low-code and full-code options.
  • Cloudera Data Engineering: Builds, orchestrates, and governs data pipelines with Apache Spark on Iceberg.
  • Cloudera Operational Database: Powers real-time applications with a NoSQL solution for hybrid environments.
  • Cloudera Data Lineage: Manages data lineage and metadata across hybrid environments.

Used for

  • Deploying and scaling private AI applications, agents, and assistants
  • Analyzing large amounts of data for many concurrent users
  • Managing and understanding data lineage and metadata
  • Powering real-time, critical applications

Who it is for

  • Financial services organizations
  • Telecommunications companies
  • Insurance companies
  • Large enterprise organizations

From Cloudera's own website, read 2026-10-23. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Cloudera. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

www.cloudera.com
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse
DeploymentHybrid

We publish a detail only when we can point at the page it came from. Know when Cloudera was founded or where it is based? Send it to us, free.

Keep reading

Buyer Guide
Cloud Data Warehouse
Toolkits
Frameworks and templates

Capability models, architecture templates and IT strategy tools to download.

Research
Pricing transparency in enterprise software

Which vendors publish their prices, and what that tells a buyer.