Implementing A Data Warehouse
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Data Warehouse Systems
With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.
Data Warehousing
Author: Mark Humphries
language: en
Publisher: Prentice Hall Professional
Release Date: 1999
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Implementing a Data Warehouse
The purpose of this book is to document the methodology and chronology of work activity used by the author to successfully implement a Data Warehouse. Each of the eleven steps of the methodology is reviewed in the book, often using actual working documents as examples. The book contains lessons learned (both good and bad) as well as measures of success for each step. An essential aspect of DW project implementation (and other IT projects as well) is using established business practices to manage development and implementation. Discussion of use of these "due diligence" practices in Step 1 establishes the foundation for starting the DW project with the proper levels of management oversight. Step 2 presents examples of business models necessary for the DW developer to understand the needs of the business that the DW will serve. Other DW books describe the data modeling process but neglect to provide modeling instruction and actual examples to insure that the DW is properly aligned with business needs. An elegant data warehouse that doesn't meet the needs of the business is wasted effort. Step 3 documents and displays the level of detail needed to define CSF's (Critical Success Factors) and KPI's (Key Performance Indicators). If calculations for these important metrics are not defined in detail, and consensus to use them is not reached, then again, the most elegant data warehouse implementation is a wasted effort. In addition, developing and documenting functional requirements is essential in identifying legacy system reporting deficiencies. Step 4 describes how to access and display field level information on the iSeries platform. Actual shots of the resulting screens are shown. Step 5 presents the functional contents of an RFP for a Data Warehousing tool-set. Step 6 presents the progression of work required to build a data warehouse. Step 6 also: · Describes and displays a hybrid dimensional to flat file data model that may be, in reality, the best data organizational model for a typical data warehouse. Also, a table is included showing examples of data file field cryptic names and their corresponding metadata name. · &nb