Yunhu big data platform

Focusing on multi-heterogeneous data storage and computing, it provides an ultra-high-speed engine to help governments and enterprises accelerate their digital transformation in a more comprehensive, convenient, intelligent, and secure way.

I want to consult

About the Yunhu Big Data Platform
Business Challenges
Advantages of Cloud Migration
Business scenarios
Customer cases

About the Yunhu Big Data Platform

It provides fully controllable one-stop enterprise-level big data services. It provides abundant big data components, is fully compatible with open-source interfaces, is independent and controllable, and enhances enterprise-level features in terms of security, reliability, and maintainability. It can freely customize and develop according to business, help governments and enterprises quickly build massive data information processing systems, and release the value of data through real-time and non-real-time analysis and mining of massive information data.

Business Challenges

The trade-off between data openness and privacy

Limited by administrative monopoly and commercial interests, the degree of data openness is low. Policies and regulations are not perfect, and there is a lack of corresponding legislation for big data mining, which cannot ensure both sharing and abuse. It will be a major challenge in the era of big data to promote the full openness, application and sharing of data while effectively protecting the privacy of citizens and enterprises.
Low data availability and poor quality

Lack of attention in the big data preprocessing stage leads to non-standard data processing, poor data availability, poor data quality, and inaccurate data. Pre-processing of the collected data allows data analysts and miners to extract valuable information.
Serious data silos within the enterprise

Data exists in different databases and data warehouses, and the value of big data is difficult to explore. Big data requires the correlation and integration of different data in order to better play the advantages of understanding customers and business. Only by connecting data and sharing technologies and tools can we better leverage the value of enterprise big data.
Unclear data business requirements

Business departments do not understand the value of big data and its application scenarios, and it is difficult to put forward clear requirements for big data. It affects the development of enterprises in the direction of big data, hinders enterprises from accumulating and mining their own data resources, and leads to the loss of enterprise data assets.

Advantages of Cloud Migration

Secure and reliable

It has enterprise-level big data multi-tenant permission management capabilities and big data security management features, supports control of access permissions by tables/columns, and supports encryption of data by tables/columns. It has been verified for large-scale reliability and long-term stability to meet the requirements of enterprise-level high reliability.
Easy operation and maintenance

It provides a visualized big data cluster management platform to improve O&M efficiency. It supports rolling upgrades and one-click patch installation, without manual intervention, and non-stop business to ensure the long-term stability of user clusters. It provides a complete cluster monitoring and alarm system, covering hardware and big data services, and provides automated health inspection and auditing.
Low cost

Based on diversified cloud infrastructure, it provides a rich selection of computing and storage facilities, and at the same time, computing and storage are separated. It provides automatic scaling according to business peaks and valleys to help customers save idle resources on the big data platform. Clusters can be created and expanded when they are available, and destroyed and scaled down when they are used up to ensure low costs.
High performance

Based on the independent and controllable open source components, the Inspur cloud deployment environment is optimized and enhanced, and its performance is much higher than that of the open source version. One copy of data can support multiple application scenarios at the same time, and improve disk read/write and computing performance through features such as multi-level indexing, pre-aggregation, dynamic partitioning, and quasi-real-time data query, and achieve second-level response to trillions of data analysis.

Business scenarios

Real-time data analysis
Massive data computing

The platform provides real-time analysis components, efficiently and quickly processes and analyzes real-time streaming data, and promotes the realization of a variety of services, including the analysis of real-time streaming data and online monitoring and alarming; For traffic flow data analysis, it supports real-time display of traffic hotspots, optimizes traffic light timing, and guides driving routes.
The platform provides a variety of relational databases, big data databases, and big data computing components such as Flink and Spark. According to the customer’s actual business scenario, the appropriate system architecture is selected and the appropriate big data computing components are used to deploy the system for the customer to meet the customer’s requirements for the timeliness of business data computing.

Customer cases

Improve service levels and lead economic development with cloud computing technology,

See all case studies

If you have any questions about Inspur Cloud, please contact us

Are you interested in our solutions?

Please do not hesitate to contact us! We're here to help.