Basic Elements of a Data Warehouse essay
Date warehouses are defined as subject-oriented, integrated, time-variant collection of data in support of management decisions. Their architecture is based on relational database management system server functioning as central repository for information data. The central information repository is surrounded by different components designed to make the environment functional, accessible and manageable for both the operational systems and the end user query.
Unlike database that is merely a collection of related data and database system, a data warehouse is a collection of large amounts of data from multiple sources and a supporting system optimized for access and designed to support efficient extraction, processing and presentation of data for different purposes. Thus, warehouses are able to store and manage large volumes of data for complex analysis. There are several key components of data warehousing. The first component is operational source systems that hold the transactional data and have little control over the format and content of the data and maintain little historical data. Another component is data staging area that stores data and encompasses a set of process between the operational source systems and the data presentation area. The process is generally called extract-transformation-load, the first step of which is extraction. It presupposes reading and understanding the source data and copying it into the staging area for further transformation. After cleansing the data and doing other relevant manipulations the quality assured data is loaded in a data warehouse. Another essential component of a data warehouse is data presentation area that is a set of integrated data marts. The data is selected according to the business needs, it is atomic, detailed and logically organized as well as finally made available for direct querying. Data marts are sets of summarized and aggregated data that take less time and are cheaper. One more component of a data warehouse is data access tools. All data access tools query the data in the data presentation area and are the software like Cognos 10 and Roambi. Depending on the purpose they can be either elementary or sophisticated. Tools may fall into the following categories: query and reporting tools, online analytical processing tools, application development tools and data mining tools. Every data warehouse has meta data that is used for its maintaining and managing, it helps users to understand content, find data and can be classified into technical meta data and business meta data. The former contains data about warehouse for designers and administrators, while the latter gives users easy access to information. In business data warehousing is used to support management decision making and provide efficient storage, functionality and responsiveness. Moreover, data warehouses are indispensible when an enterprise has to facilitate complex and frequent adhoc queries, it provides effective query support, gives historical information for analysis, supplements disaster recovery plans etc.
Data warehouses merge data from different sources into one database and analyze development over time, they perform integration of disparate data sources. Managers and executives no longer have to make decisions based on limited data. Warehousing offers considerable flexibility in operational systems and simplify the processes of data reporting and analysis. Thus, it improves the efficiency of decision-making. Presently warehousing is still expensive and only successful corporations implement it, however, if data warehouses are designed properly and function adequately, they give a company numerous benefits, generate higher amounts of revenue, provide considerable cost and time savings etc.
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