ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 7, July 2015 Copyright to IJIRCCE DOI: 10.15680/ijircce.2015. 0307018 6483 Data Warehouse as a Generic Approach a Review Shahid Bashir Dar 1 , Ashish Sharma 2 M. Tech Student, Dept. of CSE, BIMT, Mehli, Shimla, H.P, India 1 Assistant Professor, Dept. of CSE, BIMT, Mehli, Shimla, H.P, India 2 ABSTRACT: Digitization of data resulted in the generation of massive volumes of data in less time. The heterogeneous and disperse data sources makes the scene more complicated to handle. With the advent of 21 st century enterprises realized the importance of data spread across disparate sources. Large efforts were made to integrate this data at one place for carrying out long term managerial decisions out of it. These efforts resulted in the development of data warehousing as a solution for data integration and data analytics. A number of warehousing solutions have been proposed in the last few years to analyze business data, meteorological data, clinical data, and so on. But least research has been done to develop a generic tool that can create a warehouse irrespective of enterprise and data. KEYWORDS: data warehousing, data integration, data marts. I. INTRODUCTION A data warehouse is a repository of subjectively selected data from heterogeneous systems with the intent to provide strategic business information. Data warehouses are designed to facilitate answers to ad hoc, large and complex, statistical or analytical queries to carry out analysis and reporting. Enterprises need warehouse for effective business intelligence, strategic business formulation, and critical business decisions in order to survive in a globally competitive market where huge volumes of continuously growing heterogeneous data needs to be stored, processed and analysed. Traditional operational systems can‟t be used for such purpose as they are meant for day-to-day business operations, data from these operational systems flows into the warehouse where it is used for strategic decision making. Traditional operational systems stores current values optimized for transactions having high access frequency and large number of users. Access types are of read, update, and delete operation having low response time in the range of sub-seconds. On contrarily data warehouse stores archived, derived, and summarized data that is optimized for complex queries usually having low access frequency and small number of users. Read operation is the only access type in warehousing taking more time in giving response from seconds to minutes. A. Goals: A successful data warehouse should provide following features [12] to any organization.  Fast and easy accessibility of information.  Should present information consistently.  Flexible and adaptive to handle day to day changes.  Better decision making.  High security. B. Data Warehouse versus Data Mart: Data from the warehouse flows into various departments for analysing their respective area. These individual departmental components refer to as data marts. A data mart is a logical subset of a warehouse that is targeted towards a single functional area [1][2][3] like sales, finance etc. Data warehouse is the union of all data marts which gathers data from broader subjects unlike data mart whose data comes from only few areas. Since data marts are small in size as compared to the data warehouse, so they are preferred for fast and easy analysis. The structure of a data mart is to suit the departmental view of data while as, a warehouse provides a corporate view of data.