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Choosing the appropriate modeling approach is often the critical factor in the success or failure of a data warehousing implementation.ata warehousing has become a standard practice for most large companies worldwide. The data stored in the data warehouse can capture many different aspects of the business process, such as manufacturing, distribution, sales, and marketing. This data reflects explicitly and implicitly customer patterns and trends, the effectiveness of business strategies and resultant practices, and other characteristics. Such data is of vital importance to the success of the business whose state it captures. This is why companies decide to engage in the relatively expensive undertaking of creating and maintaining a data warehouse, where the costs routinely reach millions of dollars [11].For a data warehousing project to succeed, it is essential to choose a suitable data modeling approach. Organizations considering a data warehousing project should examine the real differences and trade-offs between available methodologies and determine for themselves which approach is best suited for their environments. Despite the growing pervasiveness of data warehouses, there is hardly a consensus among researchers and practitioners about the most appropriate data modeling strategies for data warehousing projects. In order to help readers recognize what the choices are and the implications of making a particular selection, this article provides an impartial and concise view of the competing methodologies and the issues that drive the ongoing debate about them. DATA WAREHOUSES AND DATA MARTSA typical organization maintains and utilizes a number of operational data sources. These operational data sources include the databases and other data repositories that are used

In the past decade, corporations are increasingly engaging in efforts whose aim is the analysis and wide-ranging use of big data. The majority of academic big data articles have been focused on methods, approaches, opportunities, and organizational impact of big data analytics. In this article, the focus is on the ability of big data (while acting as a direct source for impactful analysis) to also augment and enrich the analytical power of data warehouses.