Cloud Data Warehouse

Neusoft Select Data Warehouse (Neusoft Select for short) is a distributed database based on the MPP architecture and applicable to primitive cloud environment. It is developed by a Neusoft expert team using the Pivotal Greenplum open source platform, to which, Neusoft owns the proprietary intellectual property rights. It can assist enterprises in the management and parallel processing of massive data, laying a solid data base for the flexible extension of IT architectures. 

Neusoft Select supports multiple application scenarios such as private cloud, public cloud, etc., to reduce the management and O&M costs of enterprises. 


High performance 

Neusoft Select adopts an unshared/MPP architecture, supports storage by rows/columns, linear extension of inquiry/loading and Scatter/Gather parallel data flow technology. It has displayed a good performance in client management, BI, ODS, data warehouse, data mart, etc.  

High reliability

The product has the multi-layer fault tolerant and redundant capabilities, which can, in case of a software/hardware fault, ensure the automatic operation of data warehouse system, thus the data intactness. 


The SQL standard, client access and third-party tools are supported to realize the uniform distribution of data to all nodes for automatic parallel processing by the system. Moreover, the workload of DBA of Neusoft Select is light, because complicated tuning is not required. 


Neusoft Select provides a Web-based highly-interactive performance monitoring tool and supports the SNMP protocol, real-time/historic view inquiry and e-mail notification. 


By virtue of X86 architecture and Greenplum database, Neusoft Select has an optimal dynamic horizontal extensibility. The linear increase of the cluster capacity and the processing performance can be realized through low-cost node expansion, to meet the requirements of the clients at different stages of business development. 

Unified analysis and processing 

A unified platform is provided for data warehouse, market, ELT, text mining and statistical computing. SQL, MapReduce, R, etc. can be used on all layers to analyze data in parallel. 


  • Industrial SQL and standard database interfaces (SQL, ODBC, JDBC, and DBI); 
  • Schema (star, snowflake, 3NF, hybrid); 
  • Indexing (B-Tree, Bitmap, etc.); 
  • High-performance loading of large-scale parallel data; 
  • Extensibility and multiple languages, to achieve the complicated inquiry of large-scale data. 
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