HP Enterprise Data Warehouse Appliance

Found this off the Microsoft SQL web site.  Looks like HP and Microsoft are offering a special server and software configuration for data warehousing.  

  • Reduce your Business Intelligence implementation time with an integrated solution.

  • HP Enterprise Warehouse Appliance offers a complete solution for Data Warehousing and Business Intelligence thanks to deep integration with Microsoft BI tools such as PowerPivot for Excel, SQL Server Analysis Services, Reporting Services and Integration Services.

  • Extend your Data Warehouse solution with complementary tools such as Master Data Services for MDM and StreamInsight for managing streaming data.

  • Source: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/appliances/hp-pdw.aspx 

Take advantage of the new HP Enterprise Data Warehouse Appliance optimized for SQL Server 2008 R2 Parallel Data Warehouse. Leverage this new appliance to build enterprise data warehouses that scale with your data, improve query performance for users and reduce your IT cost. The HP Enterprise Data Warehouse appliance offers massive scale at low cost for single rack appliances up to large scale Data Warehouses.

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One comment

  1. The concept of data warehousing dates back to the late 1980s [1] when IBM researchers Barry Devlin and Paul Murphy developed the “business Data Warehouse “. In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from “data marts” that were tailored for ready access by users

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