Lecture #3 - DIMENSIONAL MODELING - SS ZG515 Data...

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SS ZG515: Data Warehousing Data Warehouse Design  An introduction to Dimensional Modeling Data Warehouses are not easy to build. Their design requires a way of thinking that is  just opposite to manner in which traditional computer systems are developed. Their  construction requires radical restructuring of vast amounts of data, often of dubious or  inconsistent quality, drawn from numerous heterogeneous sources. Their implementation  strains the limits of today’s IT. Not surprisingly, a large number of data warehouse  projects fail. Successful data warehouses are built for just one reason: to answer business  questions. The type of questions to be addressed will vary, but the intention is always the  same. Projects that deliver new and relevant information succeed. Projects that do no,  fail. To deliver answers to businesspeople, one must understand their questions. The DW  design fuses business knowledge and technology know-how. The design of the data  warehouse will mean the difference between success and failure. The design of the data warehouse requires a deep understanding of the business. Yet the  task of design is undertaken by IT professionals, but not business decision makers. Is it  reasonable to expect the project to succeed? The answer is yes. The key is learning to  apply technology toward business objectives. Most computer systems are designed to capture data, data warehouses are designed to for  getting data out. This fundamental difference suggests that the data warehouse should be  designed according to a different set of principles.  Dimensional Modeling is the name of a logical design technique often used for data  warehouses. It is different from entity-relationship modeling. ER modeling is very useful  for transaction capture in OLTP systems. Dimensional Modeling is the only viable technique for delivering data to the end users in  a data warehouse. Comparison between ER and Dimensional Modeling The characteristics of ER Model are well understood; its ability to support operational  processes is its underlying characteristic.  The conventional ER models are constituted to  (a) Remove redundancy in the data model (b) Facilitate retrieval of individual records having certain critical identifiers and (c) Therefore, optimize online transaction processing (OLTP) performance In contrast, the dimensional model is designed to support the reporting and analytical  needs of a data warehouse system.  Page 1 of 7
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SS ZG515: Data Warehousing Why ER is not suitable for Data Warehouses? End user cannot understand or remember an ER Model. End User cannot 
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This note was uploaded on 04/21/2010 for the course CSIS SS G515 taught by Professor Prof.yash during the Winter '10 term at Birla Institute of Technology & Science, Pilani - Hyderabad.

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Lecture #3 - DIMENSIONAL MODELING - SS ZG515 Data...

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