Data modeling is creating a visual representation of the data structures in a computer system. It involves identifying the various types of data to be stored, as well as the relationships between these data types.
A data model can take many forms, including diagrams or other visual representations depicting the various data structures and their relationships.
Data modeling helps in multiple computer systems, from web applications to large-scale enterprise software systems. The main goal of data modeling is to ensure that it organizes data as effectively and efficiently as possible to be easily accessed and manipulated.
There are several types of data modeling, each serving different purposes and focusing on various aspects of data representation and organization. Here are some commonly used types of DM:
Conceptual data modeling focuses on capturing the high-level, business-oriented view of the data. It aims to identify the main entities, their attributes, and the relationships between them. Conceptual models mainly represented using Entity-Relationship (ER) diagrams or similar notations.
Logical modeling builds upon the conceptual model and provides a more detailed representation of the data structures and relationships. It defines the logical organization of data elements, such as tables, columns, primary keys, foreign keys, and relationships.
The goal is to create a technology-independent model to implement various database management systems.
Physical modeling focuses on translating the logical data model into a specific database implementation. It involves defining the physical storage structures, data types, indexes, partitions, and other implementation details specific to a particular database technology.
Physical data models optimize the performance and storage efficiency of the database system.
Dimensional data modeling commonly used in data warehousing and business intelligence applications. It emphasizes the organization and representation of data for reporting and analysis purposes.
Dimensional models typically consist of fact tables (containing numerical measures) and dimension tables (describing the context of the actions).
Hierarchical modeling represents data in a hierarchical structure, where each record has a parent-child relationship with other records. This type of modeling commonly used in hierarchical databases and file systems.
Network modeling represents data with complex relationships using a network or graph-like structure. It allows for many-to-many relationships between entities and often used in network databases.
Object-oriented modeling represents data regarding objects, classes, and their relationships. Object-Oriented DM is applicable when the data and application logic are closely tied together, typically in object-oriented programming languages and databases.
ER modeling is a widely used technique for conceptual and logical DM. It represents entities as rectangles, attributes as ovals, and relationships between entities as lines connecting them.
ER modeling helps visualize the entities, their attributes, and the relationships between them.
UML is nothing but general-purpose modeling language that can be used for DM. It provides a set of notations and diagrams to represent various aspects of a system, including data.
UML class diagrams are often used for modeling data structures, relationships, and constraints.
Dimensional modeling is commonly used in data warehousing and business intelligence applications. It involves identifying and representing the data’s dimensions (descriptive attributes) and facts (numeric measures).
Dimensional modeling typically uses star or snowflake schemas to structure data for efficient querying and analysis.
DFDs are used to model the data flow within a system. They show how data moves between processes, data-stores, and external entities. DFDs can be used to model the data flow in both conceptual and logical DM.
Object-oriented modeling represents data as objects and defines their attributes and behaviors. This technique is closely tied to object-oriented programming languages and is useful when data and application logic are closely interconnected.
Object-oriented modeling techniques include class diagrams, object diagrams, and interaction diagrams.
Relational modeling is used to design relational databases. It involves identifying entities, attributes, and relationships and organizing them into tables with appropriate primary and foreign key constraints.
Relational modeling follows normalization principles to eliminate data redundancy and ensure data integrity.
Data Vault modeling is a technique for modeling enterprise data warehouses. It focuses on providing a flexible and scalable structure for storing historical data.
Data Vault modeling involves the identification of hubs (central business keys), links (associative relationships), and satellites (descriptive attributes).
