The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses
Wiley, 16. 2. 1996 - Počet stran: 416
". . . one of the definitive books of our industry. If you take the time to read only one professional book, make it this book." -W. H. Inmon
One of the most dramatic new developments in database design, the dimensional data warehouse is a powerful database model that.significantly enhances managers' ability to quickly analyze large, multidimensional data sets. Written by the leading proponent of this revolutionary new approach, this valuable book/CD toolkit outfits you with all the nuts-and-bolts information you need to design, build, manage, and use dimensional data warehouses for virtually any type of business application, as well as software for querying dimensional data warehouses.
Employing many real-life case studies of data warehouses, Ralph Kimball provides clear-cut guidelines on how to model data and design data warehouses to support advanced multidimensional decision support systems. Beginning with the relatively simple example of a data warehouse for a grocery store, he progresses, step-by-step, through an increasingly complex array of business applications in retail, manufacturing, banking, insurance, subscriptions, and airline reservations. By the end of the book, you will have mastered the full range of powerful techniques for creating, controlling, and navigating dimensional business databases that are easy to understand and navigate.
On the CD-ROM you'll find:
* Software for querying dimensional data warehouses.
* Working models of all the databases described in the book.
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THE GROCERY STORE
The Product Dimension
The Promotion Dimension
Další části 22 nejsou zobrazeny.
actually additive aggregate allowances answer set application attributes average base build calculation called changing Chapter claim column combination comparisons complex constraint contains costs coverage create data warehouse database DBMS defined describe detail developed dimension table dimensional dimensional database drilling end user example Extended extract fact table fields Figure format grain grocery header hierarchy identified important individual interviews inventory invoice join load look manufacturing measures million month original performance possible present product dimension promotion Quantity query tool questions reason records relational represent result schema separate ship shipments simple single snapshot Star Tracker status step track transaction typical usually values vendors