A COMPARATIVE ANALYSIS OF TRADITIONAL AND CLOUD DATA WAREHOUSE KHAWAJA UBAID UR REHMAN 1 , UMAIR AHMAD 1 ,SAJID MAHMOOD 2 1 Department of Computer Science, University of Management and Technology, Lahore, Punjab, Pakistan 2 Department of Informatics and Systems, University of Management and Technology, Lahore, Punjab, Pakistan Email: ubaid.rehman@umt.edu.pk, 15026050017@umt.edu.pk, sajid.mahmood@umt.edu.pk ABSTRACT. In the age of emerging technologies, the amount of data is increasing very rapidly. With the passage of time, the methods of data handling are getting improved. Prediction analysis is quite a tough task, but it also yields interesting results. Different sectors like financial services, transportation, health and education are generating large amount of data. The emergence of web 2.0 (social web) made it possible for users and researchers to analyze and predict huge amount of data. The domain of Business Intelligence is core technology for users who want to extract useful information for decision making regarding their businesses. Data warehouse provides an insight into the business processes using the historical data. However, traditional data warehouse may not be suitable for the data analysis needs because of the evolving requirement of industry. It cannot be scaled up or down. Moreover, it cannot handle the increasing number of users. A new kind of data warehouse with design and implementation aspects has been emerged, called as cloud data warehouse. The cloud data warehouse model has evolved with the passage of time, which affects the application and business domains as well. The cloud data warehouse has evolved to control the large scale data. It can be scaled up or down at any time and also it has no limitation on increasing number of users. In this review paper, we have compared traditional and cloud data warehouse. We can conclude that the ultimate future of data warehouse is cloud data warehouse. Keywords: Data Warehouse (DWH); Traditional Data Warehouse (TDWH); Cloud Data Warehouse (CDWH); Online Analytical Processing (OLAP) Introduction. Data warehousing is a standout amongst the most modern areas in the computing industry nowadays. For business directors, it has been given significant improvement to their business processes, while information system managers consider it high quality method to overwhelm the standard obstruction for presenting enterprise records for executives and other end customers. In computing, DWH is being used for reporting and data analysis, which is considered an essential factor of business intelligence. DWHs serve as the repository to store historical, sometimes current also, data from multiple sources in a single repository. The current data warehouse is more profitable and fascinating then its traditional data warehouse [1]. Bill Inmon quoted “A data warehouse is a subject-oriented, integrated, time varying, non-volatile collection of data that is used primarily in organizational decision making” [2]. The data warehouse supports OLAP, the efficacy and performance requirements unalike OLTP. A traditional data warehouse provides full support of SQL, but traditional data warehouses are not scalable. It requires a lot of time to configure, optimize and manage the system. Nowadays organizations are moving towards cloud data warehouse. The idea of a cloud data warehouse was introduced for this. A cloud data warehouse is entirely different from traditional data warehouse. Using cloud data warehouse customers can get data from different time zones and geographic. The criteria for selecting a cloud data warehouse is 34 VAWKUM Transactions on Computer Sciences http://vfast.org/journals/index.php/VTCS@ 2018, ISSN(e):2308-8168, ISSN(p): 2411-6335 Volume 15, Number 1, January-April 2018 pp:34-40