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Tha Basic Concepts of Data WarehousingBY: sukumar Natarajan | Category: Technology | Submitted: 2011-03-19 22:54:03
Abstract: Data warehouse is a process for assembling and managing data from various sources for the purpose of gaining of the single detailed view of an enterprise. The primary aims in building a data warehouse are to provide a single version of the truth about the enterprise information and to provide good performance for enterprise analysis to manipulate, animate and synthesize enterprise information. It may be a central enterprise-wide data warehouse for use by all the decision makers in the enterprise or it may consist of a number of smaller data warehouses is often called Data marts or Local data warehouse. A data warehouse is a place for, whereas data warehousing describes the process of defining, populating, and using a data warehouse. A data warehouse may be built as a centralized, a data warehouse with data marts is a distributed data warehouse depending on needs of an enterprise. On the other-hand, it is suitable for a large international organization with offices in several countries. Data warehousing is a relatively new technology that involves a number of steps. Data warehouses and on-line analytical processing (OLAP) tools have become essential elements of decision support systems. Traditionally, data warehouses are refreshed periodically by extracting, transforming, cleaning and consolidating data from several operational data sources. The data in the warehouse is then used to generate reports, or to rebuild multidimensional (data cube) views of the data for on-line querying and analysis. The main components • Operational data sources for the DW is supplied from mainframe operational data held in first generation hierarchical and network databases, departmental data held in proprietary file systems, private data held on workstations and private serves and external systems such as the Internet, commercially available DB, or DB associated with and organization's suppliers or customers Operational Data Store(ODS) is a repository of current and integrated operational data used for analysis. • It is often structured and supplied with data in the same way as the data warehouse, but may in fact simply act as a staging area for data to be moved into the warehouse. Data flows Inflow- The processes associated with the extraction, cleansing, and loading of the data from the source systems into the data warehouse. Upflow- The process associated with adding value to the data in the warehouse Through summarizing, packaging , packaging, and distribution of the data Downflow-The processes associated with archiving and backing up of data in the warehouse Outflow- The process associated with making the data available to the end-users. Tools and Technologies The critical steps in the construction of a data warehouse: Extraction Cleansing Transformation After the critical steps, loading the results into target system can be carried out either by separate products, or by a single, categories: Code generators Database data replication tools Dynamic transformation engines Application: 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 applications with specific performance or loading requirements • data marts that have outgrown their present environment • data warehouses or 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 in many areas as it evolves: • Vendors have started moving toward using commodity technologies rather than proprietary assembly of commodity components 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 2009With a lower total cost of ownership, reduced maintenance and high performance to address business analytics on growing data volumes most analysts believe that DW appliances will gain market share - though TeraData maintain their leadership position. Vendors have begun providing the ability to incorporate 'in-database' analytic algorithms to take advantage of their MPP architectures, eliminating the need to extract large datasets into traditional analytic and data mining platforms such as SAS. Article Source: http://www.writearticles.org/ About Author / Additional Info: Comments on this article: (0 comments so far)
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