Data Warehouse Design

Overview

Foreword by Mark Stephen LaRow, Vice President of Products, MicroStrategy

"A unique and authoritative book that blends recent research developments with industry-level practices for researchers, students, and industry practitioners." Il-Yeol Song, Professor, College of Information Science and Technology, Drexel University

Table of contents

Chapter 1. Introduction to Data Warehousing
Chapter 2. Data Warehouse System Lifecycle
Chapter 3. Analysis and Reconciliation of Data Sources
Chapter 4. User Requirement Analysis
Chapter 5. Conceptual Modeling
Chapter 6. Conceptual Design
Chapter 7. Workload and Data Volume
Chapter 8. Logical Modeling
Chapter 9. Logical Design
Chapter 10. Data-staging Design
Chapter 11. Indexes for the Data Warehouse
Chapter 12. Physical Design
Chapter 13. Data Warehouse Project Documentation
Chapter 14. A Case Study
Chapter 15. Business Intelligence: Beyond the Data Warehouse
Glossary
Bibliography
Index

Biographical note

Matteo Golfarelli is an associate professor of Computer Science and Technology at the University of Bologna, Italy, where he teaches courses in information systems, databases, and data mining.
Stefano Rizzi is a full professor of Computer Science and Technology at the University of Bologna, Italy, where he teaches courses in advanced information systems and software engineering.

Back cover copy

Plan, Design, and Document High-Performance Data WarehousesSet up a reliable, secure decision-support infrastructure using the cuttingedge techniques contained in this comprehensive volume. Data Warehouse Design: Modern Principles and Methodologies presents a practical design approach based on solid software engineering principles. Find out how to interview end users, construct expressive conceptual schemata and translate them into relational schemata, and design state-of-the-art ETL procedures. You will also learn how to integrate heterogeneous data sources, implement star and snowflake schemata, manage dynamic and irregular hierarchies, and fine-tune performance by materializing and fragmenting views.Work with data- and requirement-driven methodological approachesCreate a reconciled database to boost data mart architectureCapture and expressively represent end-user requirementsBuild a conceptual data mart schema using the Dimensional Fact ModelEstimate data mart volume and workloadImprove performance using advanced logical modeling techniquesExtract, transform, cleanse, and load data from operational sourcesUse sophisticated indexing techniques to optimize query execution plansComprehensively document data warehouse projectsDiscover innovative business intelligence techniques