Data WareHouse :: Technology, Database

Being a market leader today requires competitive advantage over cope with organizations. By investing in entropy warehouses, organizations can better phone the trends in market and offer services best suited to the of necessity of their customers. A Data Warehouse (DW) can be defined as a subject-oriented, non-volatile entropybase having records over years 1,2. DWs support the strategic decision-making process and befriend to answer questions such as Who was our best customer for this item run low year?3.Different DW arrangements consists of unalike components, however, few core components are divided by most DW systems. The first component is the data sources. DW receives input from different data sources (such as Point-Of-Sales (POS) systems, Automated Teller Machines (ATM) in banks, checkout terminals etc). The siemens component is the data staging area. The data comes from data sources and it is placed in the staging area, where the data is treated with different tra nsformations and cleansed of any anomalies. After this transformation, the data is placed in the third component which is known as retentiveness area, which is usually a Relational Database Management System (RDBMS). This process of data extraction from data sources, transformation and finally loading in reposition area is regarded as Extract, Transform and Load (ETL). The salve data from the storage can be viewed by reporting units. Different On-line analytical Processing (OLAP) tools assist in generating reports based on the data saved in the storage area 4,5,6,7,8.We believe that examen should be innate in DW development. Thus, each of the DW components should be tested. One of the main challenges in interrogatory the DW systems is the fact that DW systems are different among organizations, each organization has its own DW system that conforms with its own requirements and needs, which leads to having differences between DW systems in several aspects (such as database tech nology, tools used, size, number of users, number of data sources, how the components are connected, etc.)9. Another big challenge that is faced by the DW testers is regarding the test data preparation. Making use of real data for testing purpose is a violation of citizens privacy laws in some countries (for example, using real data of bank accounts and other information is dirty in many countries). For a proper testing of a DW, nominal head of a huge amount of test data is necessary. In real-time environment, the system may behave differently in the presence of terabytes of data 10.