Logical | Physical Data Modeling: When a data modeler works with the client, his title may be a logical data modeler or a physical data modeler or combination of both. A logical data modeler designs the data model to suit business requirements, creates and maintains the lookup data, compares the versions of data model, maintains…
Logical and Physical Data Modeling Objects: To become a data modeler, you need to understand the concepts of a database. Before proceeding further, please visit our topics listed under the section “Database and Data Modeling” to get a fair knowledge about the database. The following table briefs about the objects used in constructing the data model…
Analysis of Data and creating Tables & Relationships: Usually in a data model, Tables (Entities) looks like rectangular or square type boxes, which contains columns(attributes) and these tables are connected by lines (relationships). Identifying relationship and Non-Identifying relationship are two types of relationships. Based on cardinality, relationship are broadly classified into one to one relationship, one…
Data Modeling Development Cycle Following are the important phases in the Data Model Development Life Cycle. Gathering Business Requirements Conceptual Data Modeling Logical Data Modeling Physical Data Modeling Development of the schema or the database Maintenance of the data model as per the changes. 1. Gathering Business Requirements – First Phase: Data Modelers have to…
SuperType and SubType in Data Modeling: At times, few entities in a data model may share some common properties (attributes) within themselves apart from having one or more distinct attributes. Based on the attributes, these entities are categorized as Supertype and Subtype entities. Supertype is an entity type that has got relationship (parent to child…
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…
