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Vertica Systems is an analytic database management software company.〔''Network World'' staff: "New database company raises funds, nabs ex-Oracle bigwigs”, () ''LinuxWorld'', February 14, 2007〕〔Brodkin, J: "10 enterprise software companies to watch", () ''Network World'', April 11, 2007〕 Vertica was founded in 2005 by database researcher Michael Stonebraker, and Andrew Palmer. Former CEOs include Ralph Breslauer and Christopher P. Lynch. Vertica was acquired by Hewlett Packard on March 22, 2011.〔( HP News Release: “HP to Acquire Vertica: Customers Can Analyze Massive Amounts of Big Data at Speed and Scale” Feb. 2011 )〕〔 (HP News Release: “HP Completes Acquisition of Vertica Systems, Inc.” March 22, 2011. )〕 The acquisition expanded the HP Software software portfolio for enterprise companies and the public sector.〔(ComputerWorld.com: “Update: HP to buy Vertica for analytics.” Kanaracus. Feb. 2011. )〕 ==Products== The cluster-based, column-oriented Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications. The product claims to drastically improve query performance over traditional relational database systems, provide high-availability, and petabyte scalability on commodity enterprise servers. Its design features include: * Column-oriented storage organization, which increases performance of sequential record access at the expense of common transactional operations such as single record retrieval, updates, and deletes.〔Monash, C: "Are row-oriented RDBMS obsolete?" () ''DBMS2'', January 22, 2007〕 * Standard SQL interface with many analytics capabilities built-in, such as time series gap filing/interpolation, event-based windowing and sessionization, pattern matching, event series joins, statistical computation (e.g., regression analysis), and geospatial analysis. * Out-of-place updates and hybrid storage organization, which increase the performance of queries, insertions, and loads, but at the expense of updates and deletes. * Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatype are stored together and because updates to the main store are batched.〔Monash, C: "Mike Stonebraker on database compression – comments”,()''DBMS2'', March 24, 2007〕 * Shared nothing architecture, which reduces system contention for shared resources and allows gradual degradation of performance in the face of hardware failure. * Easy to use and maintain through automated data replication, server recovery, query optimization, and storage optimization. * Support for standard programming interfaces ODBC, JDBC,ADO.NET, and OLEDB. * High performance and parallel data transfer to statistical tools such as Distributed R, and the ability to store machine learning models, and use them for in-database scoring. Vertica's specialized approach aims to significantly increase query performance in data warehouses, while reducing the total cost of ownership by reducing the hardware footprint. One example of a use case detailed in a research paper shows a performance improvement of hundreds of times with Vertica in a specific application due to the use of the vertical DBMS approach.〔(One Size Fits All? Part 2: Benchmarking Results (sect. 3.1) )〕 As of late 2011, the Vertica Analytics Platform Community Edition〔http://www.vertica.com/content/vertica-announces-community-edition-version-of-vertica-analytic-database/〕 is available for free with certain limitations, such as a maximum of one terabyte of raw data, three-node (servers) cluster, and limited support. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Vertica」の詳細全文を読む スポンサード リンク
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