Data Warehousing Fundamentals: A Comprehensive Guide for IT ProfessionalsGeared to IT professionals eager to get into the all-important field of data warehousing, this book explores all topics needed by those who design and implement data warehouses. Readers will learn about planning requirements, architecture, infrastructure, data preparation, information delivery, implementation, and maintenance. They'll also find a wealth of industry examples garnered from the author's 25 years of experience in designing and implementing databases and data warehouse applications for major corporations. Market: IT Professionals, Consultants. |
From inside the book
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Page 214
... product brand ; nevertheless , package size and product brand could both be attributes of the product dimension table . Not normalized . The attributes in a dimension table are used over and over again in queries . An attribute is taken ...
... product brand ; nevertheless , package size and product brand could both be attributes of the product dimension table . Not normalized . The attributes in a dimension table are used over and over again in queries . An attribute is taken ...
Page 232
... dimension may have a large number of attributes . In either case , you may declare the dimension as large . There are ... DIMENSION Hierarchy for Finance Product Key Product Description Product 232 DIMENSIONAL MODELING : ADVANCED TOPICS.
... dimension may have a large number of attributes . In either case , you may declare the dimension as large . There are ... DIMENSION Hierarchy for Finance Product Key Product Description Product 232 DIMENSIONAL MODELING : ADVANCED TOPICS.
Page 235
... product , order , sales territories , promotional campaigns , and so on . Most of these fields wind up in the dimension tables . You ... Product dimension : THE SNOWFLAKE SCHEMA 235 Junk Dimensions The Snowflake Schema Options to Normalize.
... product , order , sales territories , promotional campaigns , and so on . Most of these fields wind up in the dimension tables . You ... Product dimension : THE SNOWFLAKE SCHEMA 235 Junk Dimensions The Snowflake Schema Options to Normalize.
Contents
The Compelling Need for Data Warehousing | 1 |
The Building Blocks | 19 |
Review Questions | 37 |
Copyright | |
26 other sections not shown
Other editions - View all
Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals Paulraj Ponniah Limited preview - 2004 |
Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals Paulraj Ponniah No preview available - 2004 |
Common terms and phrases
aggregate algorithms analysis applications architecture attributes backup business dimensions changes cluster columns complex components create data cleansing data elements data extraction data loading data marts data mining data model data quality data sources data staging data structures data transformation data warehouse environment data warehouse project data warehousing database DBMS decision deployment dimension table dimensional model end-users example fact table Figure files functions incremental loads information delivery integrated interface marketing MDDB methods metrics MOLAP multidimensional OLAP system OLTP online analytical processing operational systems options package diagram performance physical model pilot platform primary key product dimension programs project team queries and reports records relational ROLAP selection server source data source systems specific staging area standards STAR schema strategic information summary techniques tion types usage users values vendors ware Web-enabled data warehouse