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In computing, a data warehouse appliance consists of an integrated set of servers, storage, operating system(s), DBMS and software specifically pre-installed and pre-optimized for data warehousing (DW). Alternatively, the term can also apply to similar software-only systems[1] — purportedly very easy to install on specific recommended hardware configurations or preconfigured as a complete system - a true appliance.[2] [3] DW appliances provide solutions for the mid-to-large volume data warehouse market, offering low-cost performance most commonly on data volumes in the terabyte to petabyte range. Contents 1 Appliance technology 2 History 3 Benefits 3.1 Performance - Out of the box 3.2 Service - One number to call 3.3 Reduction in costs 3.4 Parallel performance 3.5 Reduced administration 3.6 Built-in high availability 3.7 Scalability 3.8 Rapid time-to-value 4 Application Uses 5 Trends 6 See also 7 References 8 External links // Appliance technology Most DW appliance vendors use massively parallel processing (MPP) architectures to provide high query performance and platform scalability. MPP architectures consist of independent processors or servers executing in parallel. Most MPP architectures implement a "shared-nothing architecture" where each server operates self-sufficiently and controls its own memory and disk. Shared-nothing architectures have a proven record[citation needed] for high scalability and little contention. DW appliances distribute data onto dedicated disk storage units connected to each server in the appliance. This distribution allows DW appliances to resolve a relational query by scanning data on each server in parallel. The divide-and-conquer approach delivers high performance and scales linearly as new servers are added into the architecture. Other DW appliance vendors use specialized hardware and advanced software, instead of MPP architectures. This approach is able to achieve MPP performance in a much smaller form factor.[4]. The first vendor to market with a data warehouse appliance featuring specialized SQL hardware was Netezza in 2003 through leveraging FPGA technology as sophisticated projection and restriction filters, minimizing data movement and I/O within the system. Kickfire followed in 2008 with what they deem a dataflow "sql chip").[5][citation needed] MPP database architectures have a long pedigree. Teradata, Tandem, Britton Lee, and Sequent offered MPP SQL-based architectures in the 1980s. Open source and commodity components have aided a re-emergence of MPP data warehouses. Advances in technology have reduced costs and improved performance in storage devices, multi-core CPUs and networking components. Open-source RDBMS products, such as Ingres and PostgreSQL, reduce software-license costs and allow DW-appliance vendors to focus on optimization rather than providing basic database functionality. Open-source Linux provides a stable, well-implemented operating system for DW appliances. History Many[who?] consider Teradata's initial product as the first DW appliance — or Britton-Lee's.[citation needed] (Note that Teradata acquired Britton Lee — renamed ShareBase — in June, 1990.[6]) Some[who?] regard Teradata's current offerings as of 2009[update] as still being other appliances, while others[who?] argue that they fall short in ease of installation or administration. Interest in the data warehouse appliance category is generally dated[by whom?] to the emergence of Netezza in the early 2000s. As of 2009[update] a second generation of DW appliances has emerged, marking the move to mainstream vendor integration. IBM integrated its InfoSphere Warehouse (formerly DB2 Warehouse) with its own servers and storage to create the IBM InfoSphere Balanced Warehouse. Netezza introduced its TwinFin platform based on commodity IBM hardware. Other DW appliance vendors have also partnered with major hardware vendors to help bring their appliances to market. DATAllegro, prior to acquisition by Microsoft, partnered with EMC and Dell and implemented open-source Ingres on Linux. Greenplum has a partnership with Sun Microsystems and implements Greenplum Database (based on PostgreSQL) on Solaris using the ZFS file system. HP Neoview has a wholly-owned solution and uses HP NonStop SQL. Kognitio offers a row-based "virtual" data warehouse appliance while Vertica, EXASOL and Paraccel offer column-based "virtual" data warehouse appliances. Like Greenplum, ParAccel partners with Sun Microsystems. These solutions provide software-only solutions deployed on clusters of commodity hardware. Kognitio’s homegrown WX2 database runs on several blade configurations. Other players in the DW appliance space include Calpont and Kickfire. Kickfire employs a column store storage engine compatible with MySQL for ease of deployment and use, in combination with specialized hardware for proven [4] performance. The market has also seen the emergence of data-warehouse bundles where vendors combine their hardware and database software together as a data warehouse platform. The Oracle Optimized Warehouse Initiative combines the Oracle Database with hardware from various computer manufacturers (Dell, EMC, HP, IBM, SGI and Sun Microsystems). Oracle's Optimized Warehouses offer pre-validated configurations and the database software comes pre-installed. In 2008 Oracle began offering a more classic appliance offering, the HP Oracle Database Machine, a jointly developed and co-branded platform that Oracle sells and supports and HP builds in configurations specifically for Oracle. [7][8] In 2009, Oracle released a second-generation Exadata system[9], based on their newly-acquired Sun Microsystems hardware. Benefits Performance - Out of the box Service - One number to call Reduction in costs The total cost of ownership (TCO) of a data warehouse consists of initial entry costs, on-going maintenance costs and the cost of changing capacity as the data warehouse grows. DW appliances offer low entry and maintenance costs. Initial costs range from $10,000 to $150,000 per terabyte, depending on the size of the DW appliance installed. The resource cost for monitoring and tuning the data warehouse makes up a large part of the TCO, often as much as 80%. DW appliances reduce administration for day-to-day operations, setup and integration. Many also offer low costs for expanding processing power and capacity. With an increased focus on controlling costs combined with tight IT Budgets, data warehouse managers sometimes need to reduce and manage expenses even while leveraging their technology as much as possible, making DW appliances a solution. Parallel performance DW appliances provide a compelling price/performance ratio.WP:EDITORIAL Many support mixed-workloads where a broad range of ad hoc queries and reports run simultaneously with loading. DW appliance vendors use several distribution and partitioning methods to provide parallel performance. Some DW appliances scan data using partitioning and sequential I/O instead of index usage. Other DW appliances use standard database indexing. With high performance on highly granular data, DW appliances can address analytics that previously could not meet performance requirements. Reduced administration DW appliances provide a single vendor solution and take ownership for optimizing the parts and software within the appliance. This eliminates the customer's costs for integration and regression testing of the DBMS, storage and OS on a terabyte scale and avoids some of the compatibility issues that arise from multi-vendor solutions. A single support-point also provides a single source for problem-resolution and a simplified upgrade-path for software and hardware. The care and feeding of DW appliances costs less than that of[weasel words]many alternate data warehouse solutions.[citation needed] DW appliances reduce administration through automated space-allocation, reduced index-maintenance and in most cases, reduced tuning and performance analysis. Built-in high availability MPP DW appliance vendors provide built-in high availability through redundancy on components within the appliance. Many offer warm-standby servers, dual networks, dual power-supplies, disk mirroring with failover and solutions for server failure. Scalability DW appliances scale for both capacity and performance. Many DW appliances implement a modular design that database administrators can add to incrementally, eliminating up-front costs for over-provisioning. In contrast, architectures that do not support incremental expansion result in hours of production downtime, during which database administrators export and re-load terabytes of data. In MPP architectures, adding servers increases performance as well as capacity. This does not always happen with alternate solutions. Rapid time-to-value Companies increasingly expect to use business analytics to improve the current cycle.[citation needed] DW appliances provide fast implementations without the need for regression- and integration-testing. In some cases, reduced tuning, reduced index creation, fast loading and reduced need for aggregation make rapid prototyping possible. Application Uses DW appliances provide solutions for many analytic application uses, including: enterprise data warehousing super-sized sandboxes which isolate power users with resource intensive queries pilot projects or projects requiring rapid prototyping and rapid time-to-value off-loading projects from the enterprise data warehouse, such as large analytical query projects that affect the overall workload of the enterprise data warehouse applications with specific performance or loading requirements data marts that have outgrown their present environment turnkey data warehouses or data marts solutions for applications with high data-growth and high-performance requirements applications requiring data warehouse encryption Trends The DW appliance market has started to shift trends[citation needed] in[weasel words]many areas as it evolves: Vendors have started moving toward using commodity technologies rather than proprietary assembly of commodity components[citation needed] Implemented applications show usage expansion from tactical and data-mart solutions to strategic and enterprise data-warehouse use. Mainstream vendor participation has become apparent as of 2009[update][citation needed]. With a lower total cost of ownership, reduced maintenance and high performance to address business analytics on growing data volumes,[weasel words]most analysts believe[citation needed] that DW appliances will gain market share - though TeraData maintain their leadership position[10]. Vendors have begun providing the ability to incorporate 'in-database' analytic algortihms to take advantage of their MPP architectures, eliminating the need to extract large datasets into traditional analytic and data mining platforms such as SAS. See also Business Intelligence (BI) Data Mining Data mart (DM) Data Warehouse References ^ Queries From Hell blog » When is an appliance not an appliance? ^ DBMS2 — DataBase Management System Services»Blog Archive » Data warehouse appliances – fact and fiction ^ Omer Trajman, Alain Crolotte, David Steinhoff, Raghunath Nambiar, Meikel Poess: Database Are Not Toasters: A Framework for Comparing Data Warehouse Appliances ^ a b [1] ^ [2] ^ Todd White (November 5 1990). "Teradata Corp. suffers first quarterly loss in four years". Los Angeles Business Journal. http://www.allbusiness.com/north-america/united-states-california-metro-areas/123633-1.html. Retrieved 2008-07-14.  ^ Oracle Performance Architect Kevin Clossen - Oracle Exadata Storage Server ^ Oracle Exadata - What is the benefit? ^ [3] ^ Gartner 2007 Magic Quadrant for Data Warehouse Database Management Systems External links Data Warehouse Appliances at the Open Directory Project DBMS2 - Positioning the data warehouse appliances v • d • e Data warehouse Creating the data warehouse Concepts Database · Dimension · Dimensional modeling · Fact · OLAP · Star schema · Aggregate Variants Column-oriented DBMS · Data Vault Modeling · HOLAP · MOLAP · ROLAP · Operational data store Elements Data dictionary/Metadata · Data mart · Sixth normal form · Surrogate key Fact Fact table · Early-arriving fact · Measure Dimension Dimension table · Degenerate · Slowly changing Filling Extract-Transform-Load (ETL) · Extract · Transform · Load Using the data warehouse Concepts Business intelligence · Dashboard · Data mining · Decision support system (DSS) · OLAP cube Languages Data Mining Extensions (DMX) · MultiDimensional eXpressions (MDX) · XML for Analysis (XMLA) Tools Business intelligence tools · Reporting software · Spreadsheet Related Persons Bill Inmon · Ralph Kimball Products Comparison of OLAP Servers · Data warehousing products and their producers This article needs additional citations for verification. Please help improve this article by adding reliable references. Unsourced material may be challenged and removed. (July 2007) This article may contain original research. Please improve it by verifying the claims made and adding references. Statements consisting only of original research may be removed. More details may be available on the talk page. (July 2008)