A Data Warehouse Appliance Can Have a Huge Positive Impact on Businesses and Organizations Essay

Businesses and musical ar pledgements of either sizes atomic number 18 be orgasm change magnitudely dep polish offent on selective information analyticals, and entropy terminus w argonhouses or product line analytic infrastructure has locomote a crease enterprise critical application for m all (if not roughly) companies. Indeed, these companies have always searched for give bug out ways to understand their customers, and anticipate their needs. They have longed to mend the speed and true put forwardment of operational finish-making. Equally grievous as cadenceliness is the depth of the information analysis.Generally, the companies want to decipher all secrets hidden at bottom the massive amounts of ever-increasing info. A info wargonhouse stratagem, which is an integrated array of hardw atomic number 18 and softwargon knowing for a specific inclination typi gossipy involving the lavishly throughput of selective information and analytic functions, ca n be used by organizations to perfect various aras of selective information executeing. Its important intent is to supersede conventional line of products give-and-take functions, such(prenominal)(prenominal) as entrepot, extract-transform-load (ETL), analysis and report.Due to its damage-effectiveness and efficiency, the information store weapon has become an important plane section of the entropy computer storage market. In this root word, I go away examine the selective information storage warehouse thingmabobs and fol pitiful its positive repair on short letter line enterprises. Introduction Since introduced in the early 1990s, entropy warehouse (DW) has proven to be the key platform for strategical and tactical decision remain firm strategys in the competitive logical argument environment today.See to a greater extent(prenominal) abridgment of Starbucks coffee company employees essayIt has become a major applied science for building info mana gement infrastructure, and resulted in many benefits for various organizations, including providing a undivided version of the truth, mitigate entropy analysis and time nest egg for users, reductions in head count, facilitation of the ontogenesis of forward-looking applications, better info, and support for customer-focused clientele strategies (Rahman, 2007). The technology has become exceedingly important in an environment where increasing competition, unpredict able-bodied market fluctuations, and changing regulatory environments are putting coerce on business organizations.Data warehouses are as advantageously becoming the of import repositories of organization/company information for info, which is obtained from a variety of operational information lineages. Business applications will find entropy warehouses more beneficial and dep unrivalled on them as the main source of information as they progress. These applications are able to suffice all sorts of info analysis, with increasing customer demands for having the most up-to-date information available in selective information warehouses. Improving information freshness within short time frames is essential to carry outing such demands. fit in to Hong et al, virtually all Fortune 1000 companies, today, have information warehouses, and many medium and small sized firms are developing them. The commit to mitigate decision-making and organisational carrying out is the fundamental business driver shag entropy warehouses. DW help managers easily discover problems and opportunities so unrivalledr, and widen the scope of their analysis. Hong also mentions that data warehouse is user-driven, meaning that users are allowed to be in maneuver of the data and will have the responsibility of determining and finding the data they need. exactly however, the data warehouses have to be designed and evaluated from the user sight in order to motivate users to be responsible for finding the da ta they need. Data warehouse is said to be wholeness of the most muscular decision-support tools to have emerged in the last decade (Ramamurthy, 2008). They are developed by firms to help managers answer important business questions which require analytics including data piece and dicing, pivoting, example-downs, roll-ups and aggregations.And these analytics are best supported by online-analytical treat (OLAP) tools. A data warehouse wash room, which is the main topic of discussion in this research, is referred to as an integrated collection of hardware and software designed for specific purposes involving the high throughput of data and analytic functions. Data warehouse lash-up has become an important segment of the data warehousing market, repayable to its live-effectiveness and efficiency. A business or organization can use a data warehouse appliance to optimize various areas of data processing.In general, the main purpose of the DW appliance is to supplant conventiona l business acquaintance (BI) functions including warehousing, extract, transform, load (ETL), analysis, and reporting. A data warehouse appliance can have a huge positive bear on on a business enterprise. Large organizations are able to faculty their data warehouse more efficiently, while assisting mid- aim companies in solving business apprehension challenges. Data warehouse is fundamentally changing the way the businesses operate, as they are increasingly adopted crosswise various companies.The purpose of this paper is to present the data warehouse appliances and how they impact businesses and organizations. In the next sections, I present a brief overview of data warehousing and the current state of BI, then I define and discuss DW appliances including its benefits, after which I describe the positive impact of DW appliances on businesses. Data Warehousing A data warehouse can basically be defined a subject-oriented, integrated, non-volatile, and time-variant collection of d ata in support of managements decisions.Unlike the on-line transaction processing (OLTP) database schemas, data warehouses are organized around subjects storing historical/summarized data for business requirement purposes. According to OBrien and Marakas, a data warehouse is a central source of data which have been cleaned, alter and cataloged so they are usable by managers/business professionals for data mining, online analytical processing, market research, and decision support. These stored data are ordinarily extracted from various operational, external, and new(prenominal) database management system of an organization.DW can be sub-divided into data marts, holding sub practises of data from the warehouse that focus on specific aspects, such as department, of a company. In general all data warehouse systems comprises of the following layers data source, data extraction, staging area, ETL, data storage, data logic, data presentation, metadata, and system trading operations layer. But the four major components let in the multi-dimensional database, ETL, OLAP, and metadata. The dimensional database applies the concept of measurement star-schema including dimension and fact tables, hierarchies for drill-down, role models, aggregates and snow flaking.It optimizes database design for better surgical procedure. The ETL process involves the extraction, transformation and fill of data with appropriate ETL tools. Data integration is one of the most important aspects of data warehouse, whereby data is extracted from multiple heterogeneous source systems and placed in a staging area where it is cleaned, transformed, p gushed, reformatted, standardized, meltd, and summarized before loading into the warehouse.OLAP (online analytical processing) tool stomachs the front-end analytical capabilities including slice and dice, drill up, drill down, drill across, pivoting, and trend analysis across time. And metadata stores information (or data) close the data in t he warehouse system. The components of a complete data warehouse architectural system are illustrated in Figure 1 below. Figure 1 An important characteristic about the data in a data warehouse is that they are static, unlike a typical database with constant changes.Once the data are gathered up, formatted for storage, and stored in the data warehouse, they will never change. The parapet is such that complex patterns or historical trends can be searched for, and hit the booksd, by queries. Data warehouses are also non-volatile in the sense that end-users cannot update the data directly, thereby being able to maintain a history of the data. A major use of the data warehouse databases is data mining, in which the data are analyzed to reveal hidden patterns and trends in historical business activity.Such analysis could be used to help managers make decisions about strategic changes in business operations in order to illuminate competitive advantages in the marketplace. Data warehousi ng is a relatively new technology that brings the vision of an entirely new (customer-centric) way of conducting business to earth, and can offer environments promising a revolution in organizational creativity and innovation (Ramamurthy, 2008).Ramamurthy also mentioned that data warehouse generally serves as an IT infrastructure technology, focused on data architecture, as it proffers a founding for integrating a diverse set of informal and external data sources, enabling enterprise-wide data access and sharing, enforcing data quality standards, providing answers to business questions, and promoting strategic thinking through CRM, data mining, and former(a) front-end BI applications. Users of the data warehouses are from virtually every business unit, amongst which information systems, marketing and sales, finance, production and operations, are the heaviest users.Current State of Business Intelligence Business Intelligence are computer based techniques used in identifying, e xtracting and analyzing business data. Sales revenue by products, department, time, region or income are such examples. The BI technologies permit historical, current and predictive views of business operations. Some common functions of BI technologies include reporting, online analytical processing, analytics, data mining, text-mining and predictive analytics. As BI aims to support better business decision-making, they can also be referred to as a decision support system.BI applications often use data gathered from data warehouses or data marts, however, not all BI applications require a data warehouse. With sources from Wikipedia, business intelligence can be applied to business purposes in order to drive business value. Amongst these business purposes include measurement, analytics, reporting, collaboration, and friendship management. BI is widely used today, mainly to describe analytic applications. According to Watson, BI is currently the top-most priority of many chief info rmation officers.In a survey of 1,400 CIOs, from Gartner Group, it was discovered that BI projects were the number one technology priority for 2007. Watson gain informs that the BI is a process which basically consists of dickens primary activities getting data in and getting data out. getting data in, also referred to as data ware housing, delivers trammel value to a business enterprise. plaques realize the full value of data from data warehouses exclusively when users and applications access the data and use it to make decisions.Getting data out receives the most attention, as it consists of business users/applications accessing data from DW to perform enterprise reporting, OLAP, doubting and analytics. The business intelligence fashion model is depicted in introduce 2. Current BI infrastructure is a patchwork of hardware, software and storage that is growing ever more complex. Figure 2 BI framework BI is continuing to evolve, and both(prenominal)(prenominal) recent de velopments are generating widespread interest, including real-time BI, business performance management, and pervasive BI.Data Warehousing Appliance A data warehouse is developed to support a broad lay out of organizational tasks. It can be referred to as an organized collection of bighearted amounts of structured data, designed and intended to support decision making in organizations. The import of information and knowledge from a data warehouse is a complex process that requires understanding of the logical schema structure and the fundamental business environment.According to Hinshaw, a data warehouse appliance, applied to business intelligence, is a machine capable of retrieving valuable decision-aiding intelligence from terabytes of data in seconds or minutes versus hours or days. The appliances represent the difference mingled with decision-making using either stale data or the freshest information workable. With sources from Wikipedia, a more standard definition of the d ata warehouse appliance is an integrated collection of hardware and software designed for a specific purpose that typically involves the high throughput of data and analytic functions.It typically consists of integrated set of servers, operating systems, data storage facilities, database management systems (DBMS), and software that is pre-installed and pre-optimized for data warehousing. DW appliances provide outcomes for the mid-to- biggish volume data warehouse market, offering low- constitute performance usually on data volumes within the terabyte range. Due to its cost-effectiveness and efficiency, the data warehouse appliance has become a critical segment of the data warehousing market.A business or an organization can use a data warehouse appliance to optimize various areas of data processing. The main purpose of a DW appliance, in general, is to supplant conventional business intelligence functions, such as warehousing, extract, transform, load (ETL), analysis, and reporting . A true DW appliance is defined as one that does not require fine- correct, indexing, partitioning, or aggregating, whereas, some former(a) DW appliances use languages such as SQL to facilitate interaction with the appliance at a database request level.With persona to Wikipedia, most data warehouse appliance vendors use massive parallel processing (MPP) architectures to provide high query performance and platform scalability. The MPP architectures consist of independent processors or servers executing in parallel, implementing a shared nothing architecture which provides an effective way to combine multiple nodes within a highly parallel environment.A DW appliance is capable of deploying up to thousands of query processing nodes in one ppliance package, compared to handed-down solutions where the cost and complexity of each additional node prevents a high level of hardware parallelism. Leveraging fully integrated data warehouse architecture, a data warehouse appliance can deliver a significant performance advantage, performing up to 100 times hurried than general-purpose data warehousing systems. Maturation With reference to Hinshaw, data warehouse appliance is specifically designed for the streaming workload of business intelligence and is built based on commodity components.It integrates hardware, DBMS and storage into one opaque device and combines the best elements of SMP and massively parallel processing (MPP) approaches into one that allows a query to be processed in the best possible optimized way. A data warehouse appliance is fully compatible with breathing BI applications, tools and data, through standard interfaces. It is simple to use and has an extremely low cost of ownership. The development of standardized interfaces, protocols and functionality is one of the most important trends in BI.In comparison to about a decade ago, there are a wealth of tools and applications using these standardized interfaces including MicroStrategy, Business Obje cts, Cognos, SAS and SPSS. And these are pair with ETL tools having standardized interfaces such as Ab Initio, Ascential and Informatica. The appliances work seamlessly with these tools and other in-house applications. A data warehouse appliance is truly scalable. The bottlenecks are the speeds of the internal buses, internal networks, and disk transfer in BI, whereas in transactional workloads, scalability is limited primarily by CPU.Reliability, which is provided by the homogenous nature of an appliance all parts of the system coming from a vendor, is also critical. A data warehouse appliance also provides simplicity for the executive directors, in that it allows administrators spend a more full-bodied time in troubleshooting complex database systems. And DBAs can be deployed to assist end users doing real-time BI. A data warehouse appliance offers the lowest cost of ownership as it has one source and one vendor, thereby reducing costs associated with support.Businesses and o rganizations will run more efficiently with the simple, efficient solution provided by a data warehouse appliance. Benefits Data warehouse appliances provide freedom to the business user. With patch-work systems, users are limited in the queries they can run due to the time required to run them. And with the time required to run a complex query reduced to seconds, users can not only run their old analysis with more iterations, but have the time to devise and run entirely new sets of analysis on farinaceous data.With sources from Wikipedia, some researched benefits of DW appliance are briefly discussed as follows Reduction in costs As a data warehouse grows, the total cost of ownership of the data warehouse consists of initial entry costs, maintenance costs, and the cost of changing cogency. DW appliances offer low entry and maintenance cost. Parallel performance DW appliances provide a compelling price/performance ratio. The vendors use several distribution and partitioning meth ods to provide parallel performance.With high performance on highly granular data, DW appliances can address analytics that could previously not meet performance requirements. Reduced Administration DW appliances can provide a single vendor solution, taking ownership for optimizing the parts and software within the appliance, thereby eliminating the customers costs for integration and regression testing of the DBMS, OS and storage on a terabyte scale. DW appliance reduces administration via change space-allocation, reduced index-maintenance and reduced tuning and performance analysis. Scalability DW appliances scale for both capacity and performance.In massive parallel processing architectures, adding servers increases performance as well as capacity. Built-in high availability Massive parallel processing DW appliance vendors provide built-in high availability via redundancy on components within the appliance. Warm-standby servers, dual networks, dual power-supplies, disk mirror ing with fail-over and solutions for server failure are offered by many. Increasingly, business analytics are expected to be used to improve the current cycle, and DW appliances provide quick implementations without the need for regression and integration testing.Also, DW appliances provide solutions for many analytic application uses. Some of these applications include enterprise data warehousing, super-sized sandboxes isolating power users with resource intensive queries, pilot projects, off-loading projects from the enterprise data warehouse, applications with specific performance or loading requirements, data marts that have outgrown their present environment, turnkey data warehouses, solutions for applications with high data growth and high performance requirements, and applications needing data warehouse encryption.Impact of Data Warehouse Appliances on Businesses and Organization Demand for data warehouse appliances is increasing, and businesses taking advantage of the benefi ts of this hardware range from a universe-wide large-scale business to the smallest individual business. Data virtualization could be a useful partner to appliances, providing a single view of information across multiple appliances. Data virtualization is also useful because it provides a stable reporting layer during normal migration exercises, such as the circumstances during addition of data warehouse appliances to the information infrastructure.As businesses today continue to process extremely large volumes of data, there is always the need to keep data warehousing costs under control while ensuring a superior BI and application performance. Scalability, flexibility, and affordability are essential requirements for designing an infrastructure capable of accompaniment next-generation BI performance. When asked why the demand for data warehouse appliance is increasing, during an interview, Robert eventide (executive vice president of marketing for Composite Software Inc. ) stat ed that it is the merging of three primary drivers at the macro level.The first is the well-reported information explosion, and the technical foul challenges involved in making this information accessible in forms that business decision-makers can easily use. Secondly, data warehouse appliances are more affordable and appealing, as the costs per terabyte and for support are coming down. And finally, recent advancements in analytics technology, notably in predictive analytics, promise to approve with the massive data volumes. Data warehouse appliances offer numerous advantages some of which are similar to benefits.Amongst the advantages include more reporting and analytical capabilities data warehouse appliance are able to handle bigger and more complex query workload, if it executes queries, Cost reductions data warehouse appliance requires a minimal amount of tuning and optimization of the database server and database design. It is also able to run most queries with a quick spe ed, Flexibility it will be easier to implement new user requests if less tuning and optimization is needed. With other database servers, a new query might lead to quite a number of technical changes, such as creating and dropping indexes, repartitioning tables, etc.Sometimes, decision is made not to implement the new request at all, due to the overwhelming work. The need for these additional technical changes is less with a data warehouse appliance. Data warehouse appliances helps support impressive BI deployments. With reference to Hinshaw, real world application examples of the positive impact of DW appliance on businesses are discussed. The rapid growth of call detail records, in the telecommunications industry, creates an appalling amount of data, which makes it difficult for companies to quickly and efficiently analyze customer and call plan information.And traditional approaches have been inefficient in processing queries on even a months data, seriously hampering an organiz ations ability to perform trend analysis to reduce customer fag and generate timely reports. However, with a DW appliance, the telecom user can analyze customer activity down to the call detail record level over a full years worth of expound data. Another industry where data warehouse appliances have begun to prove their worth, and are poised to play a bigger role in the future tense, is the retail.Hinshaw states that Brick-and-mortar and online retailers are capturing great amounts of customer transaction and supply chain information, creating a data explosion that threatens to overwhelm an average retail organization and its current IT infrastructure. But data warehouse appliances enable these retailers to manage and analyze the terabytes of information in near-real time. They are able to use the information to effectively forecast buying patterns, quickly generate targeted promotions and optimize their inventory and supply chain. Business intelligence remains the foundation fo r the success of decision making in any company.And BI, itself, relies on the underlying database architecture. Eve also presents other real world examples of positive business impact among a broad range of industries. A leading worldwide convenience foods business uses data warehouse appliances and analytic applications to acquire major business benefits in two specific areas. adept of which the company optimizes its international network of delivery routes, making the system more efficient and ensuring timely delivery of its products. Secondly, it continuously refines its merchandizing mix daily, on a retail basis, in order to maximize sales and margins.Major compact Baseball captures information about every pitch, at-bat, and fielding play within a data warehouse appliance, using this data to predict players future on-field performance. This can help teams to evaluate current and free-agent talent, refine coaching and development methods, and determine salaries, hence maximizin g their wins. Also, a global freight, transportation, and logistics company uses data warehouse appliances to identify behavioral patterns that indicate potential dissatisfaction within its exist customer base.The customer care group then proactively takes steps to improve satisfaction before they lose their customers. Currently, smaller data warehouse appliance vendors seem to be focusing on adding functionality to their products in order to manage with the mega-vendors. However, it is anticipated that all appliance vendors will be impacted by the trend toward an inexpensive, high-performance, and scalable virtualized data warehouse implementations which use regular hardware and open source software. ConclusionIn general, data warehouse appliance is a combination hardware and software product specifically designed for analytical processing. In a traditional data warehouse implementation, the database administrator can spend a significant amount of time tuning and putting structur es around the data to get the database to perform well for large sets of users. But with a data warehouse appliance, it is the vendor who is responsible for simplifying the corporeal database design layer and making sure that the software is tuned for the hardware.In this research, a comprehensive examination/review of the data warehouse appliances, their benefits, and how they positively impact businesses and organizations, was presented. Based on this research, the negative impact of DW appliances on businesses are miserable compared to its positive impact. And there is an increasing demand for DW appliances. I believe that, in the near future, the DW appliances will become the sole platform for all business intelligence applications and requirements. I gained much knowledge and insights from researching this topic, and I intend to further my research on future impacts of DW appliance on businesses.