General
Data warehouse software realizes the unified storage, distributed deployment, centralized analysis and efficient access of enterprise data, which is the basic platform of big data system. It can provide storage solutions from GB to PB, supporting technology solutions such as Hive, MapReduce and other bulk computing, Spark memory computing, Impala and spark streaming computing.
Function characteristics
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In the aspect of data storage, it can support the storage and processing of PB level of massive amounts of data, supporting the parallel processing capabilities of SQL and MapReduce.
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It supports high concurrency and linear scaling. With the use of a common MPP parallel processing architecture, it can linearly improve the storage capacity and processing power of the system by adding nodes to the MPP architecture. Users can scale up the capacity and performance according to implementation needs.
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It has high availability, and provides Mirror mechanism protection of database layer, meaning each node data is mirrored synchronously in another node, and when an error occurs on the primary node, you can switch to the Stand by node to continue the service.
Technical Advantages
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Full-featured, multi-cloud, large-scale parallel processing data platform, can meet high concurrency, high availability, secure data storage and computing and service requirements of enterprise's massive data.
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Through quasi-real-time and real-time data loading, the data warehouse can be updated in real time, thus Dynamic Data Warehouse (ADW) can be achieved, which helps enterprises to keenly perceive the changes in the market and speed up the decision making and support response.