
Cloudera
All your data. One platform. Limitless possibilities.

Description
Cloudera offers a unified hybrid data platform tailored for large enterprises facing complex data challenges. It facilitates advanced analytics and artificial intelligence deployment at scale, operating seamlessly across various environments including public clouds, private clouds, and on-premise data centers. The platform supports modern data architectures such as open data lakehouses and scalable data meshes, enabling organizations to securely manage, streamline, and utilize their data and AI applications effectively.
By allowing users to bring AI models directly to their secured and governed data, Cloudera accelerates the development and deployment of trustworthy AI solutions. The platform provides comprehensive capabilities covering the entire data lifecycle, including data ingestion, preparation, analysis, prediction, and publishing. This empowers organizations to derive significant value from their data assets, regardless of location, supporting critical AI applications and real-time analytics necessary for informed decision-making and innovation.
Key Features
- Hybrid Data Platform: Manage data, analytics, and AI across any cloud or data center.
- Open Data Lakehouse: Securely protect, streamline, and deliver data and AI quickly.
- Scalable Data Mesh Support: Enables modern distributed data architectures.
- Unified Data Fabric Capability: Connects disparate data sources and types.
- Enterprise AI Deployment: Bring AI models to secured and governed data for rapid deployment.
- Data Engineering Tools: Orchestrate and automate complex data pipelines.
- Data Warehouse Service: Provides self-service data analytics at scale.
- AI & Machine Learning Platform: Collaboratively train, evaluate, publish, and monitor ML models.
- Streaming & Data Flow: Ingest and process real-time data from any source.
- Operational Database: Build and deploy scalable, high-performance applications.
- Data Visualization: Create and publish custom dashboards and visual applications.
- Metadata Management (via Octopai): Offers end-to-end data lineage capabilities.
Use Cases
- Managing big data across hybrid environments
- Deploying enterprise-scale AI and machine learning models
- Building and managing open data lakehouses
- Implementing scalable data mesh architectures
- Conducting customer analytics
- Analyzing IoT and connected product data
- Ensuring data security, risk management, and compliance
- Running real-time data analytics
- Powering business-critical AI applications
- Optimizing business operations with data insights
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