Data pipeline development
Cloudera Data Engineering builds and orchestrates Apache Spark pipelines on Iceberg, with scheduling, monitoring, and debugging for ETL workflows.
Cloudera
Freemium
A hybrid data and AI platform for enterprise analytics, pipelines, and private AI.
Clouderaとは？
Cloudera is an enterprise data and AI platform for workloads across public clouds, data centers, and the edge. It combines data engineering, analytics, operational databases, streaming, and AI services with shared security and governance.
Its modular hybrid offering, Cloudera Anywhere Cloud, supports organizations that need to work with data across multiple environments while retaining control over it. An open data lakehouse built on Apache Iceberg supplies data for analytics and AI, while development and inference services support private models, applications, and agents.
Clouderaでできること
AI development and inference
AI Workbench provides collaborative workspaces to build, train, and deploy models on governed data. AI Inference serves traditional models, large language models, and generative AI models.
Warehouse and operational queries
Data Warehouse provides self-service access for enterprise analytics. Operational Database supplies a NoSQL service for real-time applications in hybrid environments.
Centralized data governance
Shared Data Experience, Data Catalog, and Data Lineage combine access controls, discovery, classification, metadata, and lineage management across the data estate.
Streaming and data movement
Data Flow uses Apache NiFi to collect and move data between sources and destinations. Cloudera Streaming supports streaming workflows with Kafka and Flink.
Deployment observability
Cloudera Observability monitors deployments across hybrid environments to help teams improve resource utilization and manage costs.
利用シーン
“Prepare enterprise data for AI”
Data engineering teams can connect dispersed sources and assemble governed datasets for model development, AI applications, and downstream analytics.
“Build private assistants and agents”
AI teams can use collaborative development workspaces and inference services to develop enterprise assistants and agents while controlling access to underlying data.
“Support fraud detection”
Financial services teams can apply models to transaction data. Cloudera describes Regions Bank using its platform to improve fraud detection and customer retention.
“Accelerate reporting”
Analytics teams can use warehouse services to query large datasets for business and regulatory reporting. Bank of Montreal is a published customer example of reduced query runtime.
よくある質問
Which public clouds does Cloudera support?
Cloudera on cloud services run on AWS, Microsoft Azure, and Google Cloud. Cloudera also offers on-premises software and data services.
How is cloud usage priced?
Most listed services use hourly Cloudera Compute Unit (CCU) rates, based on cores and memory. Examples include Data Warehouse at $0.07/CCU, AI Workbench at $0.20/CCU, and AI Inference at $0.25/CCU. Rates are estimates that vary by instance type; infrastructure, networking, and related costs are additional.
How are subscriptions and support handled?
Cloud customers can pay monthly or buy prepaid credits. On-premises offerings use annual subscriptions with pricing through sales. Cloud and on-premises offerings include technical support and version, maintenance, and security updates.
What storage options are available on premises?
Cloudera Base supports HDFS, Cloudera Object Store powered by Apache Ozone, and select third-party storage. On-premises Data Services rely on Base for storage, data management, and Shared Data Experience.
クイック情報
- Product category
- Hybrid data and AI platform
- Primary users
- Enterprise data engineers, analytics teams, and AI teams
- Open table format
- Apache Iceberg
- Object storage
- Apache Ozone with S3 compatibility
- Website
- https://cloudera.com
Cloudera のトラフィック分析
トラフィックデータは参考情報としてご利用ください。
- ドメイン評価
- 82
Clouderaの代替品
Hyperstack
www.hyperstack.cloud
Hyperstack is a cloud GPU platform for running AI and machine learning workloads, including training, inference, data analytics, and model development. It also provides AI Studio, virtual machines, and managed Kubernetes for deploying and operating GPU-backed workloads.