Composite or Compound Keys: When more than one field is used to represent a key, it is referred to as a composite key.The user needs to select one of all available primary keys, and the remaining become alternate keys. Primary and Alternate Keys: Any field containing a unique record can be called as a primary key.For example, customer ID, employee number, etc. Business or Natural Keys: It is a field that uniquely identifies an entity.Keys of dimensional modeling are divided into five categories. Keys are important to understand while we learn data modeling. An entity can be called as a concept, a piece of data, or an object about which the data (and the relations surrounding the data) is stored. It is also known as the entity–relationship (E–R) diagram. We can call such types of database models as hybrid models.Īs the name indicates, the entity–relationship model is a graphical presentation of entities and their relationships. This type of a database model is known as a post-relational database model as it is not limited to tables, even though it incorporates tables. ![]() We have various kinds of object-oriented databases, namely multimedia database, hypertext database, and more. These objects have associated features and methods. The object-oriented database model consists of a collection of objects. Each record can belong to multiple sets and allows the model for conveying complex relationships. Each set consists of a parent record and multiple child records. As per the mathematical set theory, we construct the model with sets of related records. The network model can be built on the hierarchical model, wherein it allows multiple relationships among linked records which implies that it has multiple parent records. Soon after the introduction of this model, it was combined with Structured Query Language (SQL). It requires detailed knowledge of the physical data storage adopted by the organization. This model has reduced the program complexity. Here, data segments are explicitly combined with the help of tables. It doesn’t require developers to define the data path. This was initially proposed as an alternative to the hierarchical model in 1970 by an IBM researcher. But due to some inefficiencies, they are very rarely used now. During 1960s and 1970s, this database model was all the rage. ![]() You can use this type of modeling for many real-world model relationships. This order is used as the physical order for storing the database. When it comes to sibling records, they are sorted in a particular order. Here, each of the records has a single root or parent. This is a database modeling that is based on a tree-like structuring. Let’s take a glance at some of the data models that are popularly used: There are various approaches to data modeling, but the base concept remains the same for all types of models. It is created by Database Administrators and Developers. It outlines the implementation methodology in terms of tables, CRUD operations, indexes, partitioning, etc. The physical model defines how to implement a data model with the help of the database management system. This model is generally designed by Business Analysts and Data Architects. It broadly includes all kinds of data that need to be captured such as tables, columns, etc. The logical model defines how the model should be implemented. It is basically designed by Data Architects and Business Stakeholders. It mainly focuses on business-oriented entries, attributes, and relations. This level defines what needs to be present in the structure of the model in order to define and organize business concepts. It defines relational tables, stored procedures, and primary and foreign keys.It also improves performance to the core. It helps Project Managers with a better scope and quality management.A qualified data model helps in providing better consistency across all projects of an enterprise.It assists in identifying the redundant, duplicate, and missing data as well. It helps in the creation of a robust design that brings the entire data of an organization on the same platform.The data model portrays a better understanding of business requirements.Data omission can lead to incorrect results and faulty reports. The data model reduces the chances of data omission. All important data of an enterprise are accurately presented in the model. ![]() It provides a holistic picture of the data which can be used by developers to create a physical database. Visual representation of data helps improve data analysis.Primary reasons for using a data model are listed below:
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