Data Modeling Techniques for Data Warehousing by IBM Redbooks

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This brings with it the need to involve people from each of the workgroups, departments, or lines of business that will be participating in the data warehouse implementation. Decisions concerning data sources to be used, security, data structure, data quality, data standards, and an overall data model will typically need to be completed before actual implementation begins. The top down implementation can also imply more of a need for an enterprisewide or corporatewide data warehouse with a higher degree of cross workgroup, department, or line of business access to the data.

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. In this book, we will use them interchangeably. Even though it can differ across the modeling phases, usually an entity has its own business definition and a clear boundary definition that is required to describe what is included and what is not.

An attribute name should be unique in an entity and should be self-explanatory. For example, simply saying date1 or date2 is not allowed, we must clearly define each. As examples, they could be defined as the order date and delivery date. 38 Data Modeling Techniques for Data Warehousing When an instance has no value for an attribute, the minimum cardinality of the attribute is zero, which means either nullable or optional . In Figure 12, you can see the characters P, m, o, and F . They stand for primary key, mandatory, optional, and foreign key .

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