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 notice major merits through enforcing a
data warehouse. it really is mostly approved that facts warehousing offers an
excellent method for remodeling the massive quantities of information that exist in these
organizations into priceless and trustworthy info for buying solutions to their
questions and to aid the choice making strategy. a knowledge warehouse
provides the bottom for the strong information research options which are available
today akin to information mining and multidimensional research, in addition to the more
traditional question and reporting. applying those innovations in addition to data
warehousing may end up in more straightforward entry to the data you wish for more
informed determination making.
The query so much requested now could be, How do I construct a knowledge warehouse? this is often a
question that's not really easy to reply to. As you'll find during this booklet, there are
many methods to development one. in spite of the fact that, on the finish of all of the research,
planning, and architecting, you are going to come to achieve that all of it starts off with a firm
foundation. no matter if you're construction a wide centralized information warehouse, one
or extra smaller allotted information 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 is often, in any case, probably the most key
concepts in facts warehousing and what differentiates it from the extra typical
operational database and determination aid software development. that's, you
structure the knowledge and construct functions round it instead of structuring
applications and bringing information to them.
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Extra resources for Data Modeling Techniques for Data Warehousing
1 Basic Concepts An ER model is represented by an ER diagram, which uses three basic graphic symbols to conceptualize the data: entity, relationship, and attribute. 1 Entity An entity is defined to be a person, place, thing, or event of interest to the business or the organization. An entity represents a class of objects, which are things in the real world that can be observed and classified by their properties and characteristics. In some books on IE, the term entity type is used to represent classes of objects and entity for an instance of an entity type.
The relationship between two entities can be defined in terms of the cardinality. This is the maximum number of instances of one entity that are related to a single instance in another table and vice versa. The possible cardinalities are: one-to-one (1:1), one-to-many (1:M), and many-to-many (M:M). In a detailed (normalized) ER model, any M:M relationship is not shown because it is resolved to an associative entity. Figure 12 shows examples of relationships. A high-level ER diagram has relationship names, but in a detailed ER diagram, the developers usually do not define the relationship name.
The question then becomes, How should the data warehouse databases be designed to best support the needs of the data warehouse users? Answering that question is the task of the data modeler. Data modeling is, by necessity, part of every data processing task, and data warehousing is no exception. As we discuss this topic, unless otherwise specified, the term data warehouse also implies data mart . We consider two basic data modeling techniques in this book: ER modeling and dimensional modeling. In the operational environment, the ER modeling technique has been the technique of choice.
Data Modeling Techniques for Data Warehousing by Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, Eunsaeng Kim, Ann Valencic