Data Warehouse Design: Modern Principles and Methodologies
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Data Warehouse Design: Modern Principles and Methodologies : Modern Principles and Methodologies

By (author) Mattaeo Golfarelli , By (author) Stefano Rizzi

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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

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  • Mixed media product | 480 pages
  • 185.42 x 228.6 x 25.4mm | 793.78g
  • 01 Jul 2009
  • McGraw-Hill Education - Europe
  • Osborne/McGraw-Hill
  • New York
  • English
  • 0071610391
  • 9780071610391
  • 196,400

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Author Information

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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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

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