Download Data Modeling Techniques for Data Warehousing by Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, PDF

By Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, Eunsaeng Kim, Ann Valencic

Companies of all sizes and in numerous industries, in addition to government
agencies, are discovering that they could become aware of major advantages through imposing a
data warehouse. it's normally accredited that info warehousing offers an
excellent procedure for reworking the sizeable quantities of knowledge that exist in these
organizations into necessary and trustworthy details for purchasing solutions to their
questions and to aid the choice making technique. a knowledge warehouse
provides the bottom for the robust information research recommendations which are available
today resembling facts mining and multidimensional research, in addition to the more
traditional question and reporting. applying those options besides data
warehousing can lead to more straightforward entry to the knowledge you would like for more
informed selection making.
The query such a lot requested now's, How do I construct an information warehouse? this can be a
question that's not really easy to respond to. As you will see that during this ebook, there are
many ways to construction one. even though, on the finish of the entire research,
planning, and architecting, you'll come to gain that all of it starts off with a firm
foundation. no matter if you're construction a wide centralized info warehouse, one
or extra smaller disbursed facts warehouses (sometimes known as info marts), or
some mixture of the 2, you'll always come to the purpose the place you must
decide on how the knowledge is to be established. this can be, in the end, probably the most key
concepts in info warehousing and what differentiates it from the extra typical
operational database and determination help program construction. that's, you
structure the knowledge and construct purposes round it instead of structuring
applications and bringing information to them.

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Data for the data warehouse is typically extracted from operational systems and possibly from data sources external to the organization with batch processes during off-peak operational hours. It is then filtered to eliminate any unwanted data items and transformed to meet the data quality and usability requirements. It is then loaded into the appropriate data warehouse databases for access by end users. 16 Data Modeling Techniques for Data Warehousing A global warehouse architecture enables end users to have more of an enterprisewide or corporatewide view of the data.

This architecture brings with it many other functions and capabilities that can be selected. Be aware, however, that these additional choices can bring with them additional integration requirements and complexity as compared to the independent data mart architecture. For example, you will now need to consider who controls and manages the environment. You will need to consider the need for another tier in the architecture to contain, for example, data common to multiple data marts. Or, you may need to elect a data sharing schema across the data marts.

IS could, for example, provide help in cross-department security, backup and recovery, and the network connectivity aspects of the implementation. In contrast, interconnected data marts could be controlled and managed by IS. Each workgroup, department, or line of business would have its own data mart, but the tools, skills, and resources necessary to implement the data marts would be provided by IS. 1, “Architecture Choices” on page 15. The approaches to be discussed in this book are top down, bottom up, or a combination of both.

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