Dimensional Data Modeling comprises of one or more dimension tables and fact tables. Good examples of dimensions are location, product, time, promotion, organization etc. Dimension tables store records related to that particular dimension and no facts (measures) are stored in these tables. For example, Product dimension table will store information about products (Product Category, Product…
The completed relational data model is shown in the previous part and the corresponding data stored in database are shown in separate tables below. State Lookup: State Code State Name DateTimeStamp NY New York 1/1/2005 11:23:31 AM FL Florida 1/1/2005 11:23:31 AM CA California 1/1/2005 11:23:31 AM NJ New Jersey 1/1/2005 11:23:31 AM County Lookup: County Code…
Upon discussion with business analysts, data modeler can come up with the following conclusions regarding grouping and relationship between the data. These conclusions play a vital role in designing the data model as well as expanding for future scope. Many cities can be in one county. City names will be unique across the country. Many counties…
The sample source data shown in the table below provides the information about employees, their residential state, county, city and their employer names and manager names. It also describes employees working for an “American Bank” that has got many branches in several states. From data modeler point of view, analysis of the source data raises…
Relational (OLTP) Data Modeling: Relational Data Model is a data model that views the real world as entities and relationships. Entities are concepts, real or abstract about which information is collected. Entities are associated with each other by relationship and attributes are properties of entities. Business rules would determine the relationship between each of entities…
Physical Data Modeling: Physical data model includes all required tables, columns, relationships, database properties for the physical implementation of databases. Database performance, indexing strategy, physical storage and de-normalization are important parameters of a physical model. Logical data model is approved by functional team and there-after development of physical data model work gets started. Once physical data…
Logical Data Modeling: This is the actual implementation and extension of a conceptual data model. A Logical data model is the version of a data model that represents the business requirements (entire or part) of an organization and is developed before the physical data model. As soon as the conceptual data model is accepted by…
Enterprise Data Modeling: The development of a common consistent view and understanding of data elements and their relationships across the enterprise is referred to as Enterprise Data Modeling. This type of data modeling provides access to information scattered throughout an enterprise under the control of different divisions or departments with different databases and data models….
Conceptual Data Modeling: Conceptual data model includes all major entities and relationships and does not contain much detailed level of information about attributes and is often used in the INITIAL PLANNING PHASE. Conceptual data model is created by gathering business requirements from various sources like business documents, discussion with functional teams, business analysts, smart management experts…
