Data Warehouse Design: Modern Principles and Methodologies / Edition 1

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Overview

Plan, Design, and Document High-Performance Data Warehouses

Set 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 approaches
  • Create a reconciled database to boost data mart architecture
  • Capture and expressively represent end-user requirements
  • Build a conceptual data mart schema using the Dimensional Fact Model
  • Estimate data mart volume and workload
  • Improve performance using advanced logical modeling techniques
  • Extract, transform, cleanse, and load data from operational sources
  • Use sophisticated indexing techniques to optimize query execution plans
  • Comprehensively document data warehouse projects
  • Discover innovative business intelligence techniques
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Product Details

  • ISBN-13: 9780071610391
  • Publisher: McGraw-Hill Professional Publishing
  • Publication date: 5/26/2009
  • Edition number: 1
  • Pages: 480
  • Sales rank: 772,990
  • Product dimensions: 7.30 (w) x 9.10 (h) x 1.00 (d)

Meet the Author

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.

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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

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