Nowadays, any business works with large volumes of data, it is necessary to remain competitive. But in every field data has its own characteristics and peculiarities. At a certain point, a company comes to the conclusion that data needs to be modeled. This is where data models come in. You need them in order to understand them better and manage data more effectively.
What is a data model?
A data model is a set of rules for organizing data in a database management system, which includes the structure of data organization, that is, how the nature of data is arranged and what types they have, integrity, meaning, how the completeness of the database is ensured, and data management, that is, how the end user manages data within the database.
Types of models
There are three main types of data model design.
The conceptual data model includes all the basic entities and relationships, does not contain detailed attribute information, and is often used at the initial planning stage. At this stage, the concept of the entire data model or objects in the software system and the relationships between the objects are usually formed, which lays the foundation for the construction of a logical data model.
The logical data model is intended to further specify the conceptual data model. The attributes of each entity are defined based on the entity defined by the conceptual data model. The logical model takes into account all entities, attributes, and relationships, and is a ready-to-use document for creating a physical database model.
The physical data model is a concrete representation of the conceptual data model and the logical data model in the computer. This model describes a specific organizational data structure on a physical data medium. Given the constraints and requirements set by the logical model, the physical model implements the direct storage of data in the database.
Data modeling techniques
Hierarchical data models represent one-to-many relationships in a tree-like format. In this type of model, each record has a single root or parent element that is mapped to one or more child tables. Although this approach is less efficient than recently developed database models, it is still used in Extensible Markup Language (XML) and Geographic Information Systems (GIS).
The Network Model model is essentially an improvement of the hierarchical model but with its own peculiarities. For example, unlike a hierarchical model, a network data model can have multiple ancestor objects. This architecture allows us to gain in memory usage and speed, but at the same time it is complex and inflexible, it is quite difficult to change.
Relational data models were introduced in 1970 by E. F. Codd, an IBM researcher, and are still commonly used in enterprise computing databases. Relational modeling simplifies the database by combining data sections using tables without needing to comprehend the physical attributes of the data store.
Relational databases often use structured query language (SQL) to manage data. These databases are suitable for maintaining data integrity and minimizing redundancy. They are often used in cash register systems and for other types of transaction processing.
Object-oriented data models became popular in the mid-1990s due to the rise of object-oriented programming. These models are based on abstract representations of real-world entities called "objects" that are organized into classes and have related attributes. Object-oriented databases can include tables, but they can also handle more intricate relationships, particularly in multimedia and hypertext databases.
Entry-relationship data models use diagrams to represent the relationships between entities in a database. An ER model is a formal construct that does not prescribe any graphical means of its visualization. An entity-relationship diagram has been proposed as a standard graphical notation that can be used to visualize an ER model. However, other graphical notations may be used to visualize ER models, or visualization may not be used at all (e.g., only textual description).
Conclusion
Creating a model enables developers, data architects, business analysts, and other involved parties to have a simpler and clearer understanding of how the data that relates to their enterprise is interconnected. In addition, data modeling helps:
- Improve communication between developers and business intelligence teams.
- Simplify and accelerate the database design process at the conceptual, logical, and physical levels.
- Improve application performance.