Object-Oriented Data Warehouse Design: Building A Star Schema
Written for any database developer or IS manager planning to build a data warehouse,Object-Oriented Data Warehouse Designprovides just the right level of detail. Mixing practical advice for project management with a remarkably clear explanation of how data warehouses store information, this book fits the bill for anyone who wants to deliver a working system that fits the needs of its users.Data warehouses are often plagued with failure, the author argues, and his prescription for success is what makes this title worthwhile. First on the agenda is a concise tour of project management techniques (using today's iterative software methodologies) along with a practical guide to what's in store for the potential data warehouse architect as he or she works within the enterprise to combine data from disparate sources. (The author's candid sample interviews with different users of a hypothetical warehouse are alone worth the price of the book.)The remarkably clear diagrams for understanding how data is stored in a "cube" (orhypercube) within a data warehouse will help any developer or manager understand the underlying theoretical design issues. Combined with a sample star schema for a real-estate brokerage firm, this title also demonstrates a distinctly practical side. (There are numerous pages listing the specifics of tables needed for the sample project.) You'll also be able to understand the traditional star schema, where dimensions are used to query large fact tables, plus when to implement snowflake schemas to improve performance.This concise and well-diagrammed book can let virtually anyone understand how warehouses work; the material on project management may save the day in your next data warehouse project. Filled with practical information,Object-Oriented Data Warehouse Designwill be a valuable resource for any developer or project manager.--Richard DraganTopics covered: Introduction to data warehouses, advantages and design issues, decision support systems (DSS), online analytical processing (OLAP), data mining, iterative development techniques, metadata and metadata standards, object modeling, analysis and design, case study and sample interviews with data warehouse stakeholders, multiple dimensions and dimension tables, cubes and hypercubes, slicing and dicing, constellation/star vs. snowflake schemas, fact tables, design hints, parallel processing for data warehousing, star query optimization, data warehousing on the Web, and checklist for successful data warehouse design.