中文CyberEngine
Based on the web operation interface, zero coding completes the operation and maintenance of large data cluster deployment, and supports a variety of scenario-based one-click deployment and configuration capabilities of data warehouses and data lake platforms.
Cloud-native technology is adopted to support storage and accounting separation, flow batch integration, lake warehouse integration and other architectures to improve resource utilization, improve system reliability, and reduce management complexity.
Secure management of large data clusters based on Kerberos, OpenLDAP and Ranger, and user resource isolation based on multi-tenancy.
Fully integrate various big data components of Hadoop and non-Hadoop systems to meet the needs of various big data platform scenarios and provide solutions for different scenarios.
Adhering to the concept of "open source and giving back to the community", the product's deep-optimized big data components and deployment management platform are all open source, which is convenient for developers to access and integrate.
Provide comprehensive big data cluster management and monitoring functions to help users monitor the health status, resource utilization, and task execution of the cluster in real time, and improve operation and maintenance efficiency and cluster stability.
Fully integrate mainstream big data components, including Hadoop, Spark, Hive, Flink, Kafka, Doris, Hudi, Solr, etc., to quickly deploy distributed clusters of big data in multiple scenarios, and provide rich component management functions, such as version control, configuration management, log query, etc., to greatly reduce operation and maintenance costs and difficulties.
It adopts cloud-native and containerized technology, supports high scalability and fault tolerance, has the ability of cluster elasticity and scalability, and can quickly respond to business needs and fault handling.
It supports a separate architecture for storage and accounting, solves the problem of coupling storage and computing resources, improves the utilization rate of computing resources and system stability, and effectively reduces storage costs.
Supports stream batch data processing mode, enabling seamless switching between real-time data processing and batch processing, allowing users to more easily process different types of data.
Provide comprehensive big data cluster management and monitoring functions to help users monitor the health status, resource utilization, and task execution of the cluster in real time, and improve operation and maintenance efficiency and cluster stability.
Fully integrate mainstream big data components, including Hadoop, Spark, Hive, Flink, Kafka, Doris, Hudi, Solr, etc., to quickly deploy distributed clusters of big data in multiple scenarios, and provide rich component management functions, such as version control, configuration management, log query, etc., to greatly reduce operation and maintenance costs and difficulties.
It adopts cloud-native and containerized technology, supports high scalability and fault tolerance, has the ability of cluster elasticity and scalability, and can quickly respond to business needs and fault handling.
It supports a separate architecture for storage and accounting, solves the problem of coupling storage and computing resources, improves the utilization rate of computing resources and system stability, and effectively reduces storage costs.
Supports stream batch data processing mode, enabling seamless switching between real-time data processing and batch processing, allowing users to more easily process different types of data.
CyberEngine can help users build big data lake warehouses and support the rapid import and processing of big data. Support HDFS, Hive, Flink, Spark, Hudi, IceBerg and other mainstream big data components to meet users' needs for efficient, stable and scalable big data storage, query and analysis. Users can quickly access and manage data through CyberEngine, providing enterprises with efficient data storage, query, and analysis capabilities.
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