The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

Ralph Kimball, Margy Ross

Language: English

Pages: 600

ISBN: 1118530802

Format: PDF / Kindle (mobi) / ePub


Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!

The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.

  • Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence
  • Begins with fundamental design recommendations and progresses through increasingly complex scenarios
  • Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more
  • Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more

Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

conformed facts of a set of dimensional models, you have a practical and predictable framework for incrementally building complex DW/BI systems that are inherently distributed. For all that has changed in our industry, the core dimensional modeling techniques that Ralph Kimball published 17 years ago have withstood the test of time. Concepts such as conformed dimensions, slowly changing dimensions, heterogeneous products, factless fact tables, and the enterprise data warehouse bus matrix xxviii

support. For these reasons, profit and loss fact tables are typically not tackled during the early implementation phases of a DW/BI program. Chapter 6 Order Management, p 189 Chapter 15 Electronic Commerce, p 370 Multiple Currency Facts Fact tables that record financial transactions in multiple currencies should contain a pair of columns for every financial fact in the row. One column contains the fact expressed in the true currency of the transaction, and the other contains the same fact expressed

similar to those below in Figure 3-13. You can plainly see the relationship between the dimensional model and the associated query. High-quality dimension attributes are crucial because they are the source of query constraints and report labels. If you use a BI tool with more functionality, the results would likely appear as a cross-tabular “pivoted” report, which may be more appealing to business users than the columnar data resulting from an SQL statement. Retail Sales Retail Sales Facts

limits your ability to add supplemental dimensions because the additional dimensions often don’t apply at the higher grain. The predictable symmetry of dimensional models enable them to absorb some rather significant changes in source data and/or modeling assumptions without invalidating existing BI applications, including: ■ New dimension attributes. If you discover new textual descriptors of a dimension, you can add these attributes as new columns. All existing applications will be oblivious to

such as when the product is ordered, received, or sold, but not with inventory. The simplest view of inventory involves only a single fact: quantity on hand. This leads to an exceptionally clean dimensional design, as shown in Figure 4-2. Date Dimension Date Key (PK) ... Store Dimension Store Inventory Snapshot Fact Date Key (FK) Product Key (FK) Store Key (FK) Quantity on Hand Product Dimension Product Key (PK) Storage Requirement Type ... Store Key (PK) ... Figure 4-2: Store inventory

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