Building a Data Warehouse: With Examples in SQL Server (Hardcover)

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Building a Data Warehouse: With Examples in SQL Server (Hardcover)

[Question] Building a Data Warehouse: With Examples in SQL Server (Hardcover)	14/01/2008 22:34:34 (+0700) \| #1 \| 110193

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Building a Data Warehouse: With Examples in SQL Server (Hardcover)

Building a Data Warehouse: With Examples in SQL Server describes how to
build a data warehouse completely from scratch and shows practical
examples on how to do it. Author Vincent Rainardi also describes some
practical issues he has experienced that developers are likely to
encounter in their first data warehousing project, along with solutions
and advice. The RDBMS used in the examples is SQL Server; the version
will not be an issue as long as the user has SQL Server 2005 or later.
The book is organized as follows. In the beginning of this book
(Chapters 1 through 6), you learn how to build a data warehouse, for
example, defining the architecture, understanding the methodology,
gathering the requirements, designing the data models, and creating the
databases. Then in Chapters 7 through 10, you learn how to populate the
data warehouse, for example, extracting from source systems, loading the
data stores, maintaining data quality, and utilizing the metadata. After
you populate the data warehouse, in Chapters 11 through 15, you explore
how to present data to users using reports and multidimensional
databases and how to use the data in the data warehouse for business
intelligence, customer relationship management, and other purposes.
Chapters 16 and 17 wrap up the book: After you have built your data
warehouse, before it can be released to production, you need to test it
thoroughly. After your application is in production, you need to
understand how to administer data warehouse operation.
What you'll learn:

- A detailed understanding of what it takes to build a data warehouse
- The implementation code in SQL Server to build the data warehouse
- Dimensional modeling, data extraction methods, data warehouse
loading, populating dimension and fact tables, data quality, data
warehouse architecture, and database design
- Practical data warehousing applications such as business
intelligence reports, analytics applications, and customer relationship
management

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