What is a logical data model

What is data modeling?

 

Data modeling is the creation of a data model for the data to be stored in a database. This data model is a conceptual representation of:

• data objects
• The assignments between different data objects
• The rules.

Data modeling helps visualize data and enforces business rules, regulatory compliance, and government policies on the data. Data models guarantee consistency in naming conventions, standard values, semantics and security while at the same time ensuring data quality.

The data model emphasizes what data is needed and how it should be organized, not what operations must be performed on the data. The data model is like an architect's floor plan, which helps create a conceptual model and establish the relationship between data elements.

There are two types of data model techniques

1. Entity-Relationship-Model (E-R)
2. UML (Unified Modeling Language)

In this post we will deal with different topics:

• What is data modeling?
• Why is a data model used?
• Types of data models
• Concept model
• Logical data model
• Physical data model
• Advantages and disadvantages of the data model

Why use data model?
The main goal of using the data model is:
• Ensures that all data objects required by the database are represented accurately.
• Omitting data will lead to incorrect reports and incorrect results.
• A data model helps design the database on a conceptual, physical and logical level.
• The data model structure helps define the relational tables, the primary and foreign keys, and the stored procedures.
• It provides a clear picture of the basic data and can be used by database developers to create a physical database.
• It is also helpful to identify missing and redundant data.
• While creating the data model is time-consuming and time-consuming, it makes upgrading and maintaining your IT infrastructure cheaper and faster in the long run.
• Types of data models

There are mainly three different types of data models:

1. Conceptually:

This data model defines WHAT the system contains. This model is typically created by business stakeholders and data architects. The aim is to organize, define and define business concepts and rules.

2. Logical:

Defines how the system should be implemented independently of the DBMS. This model is usually created by data architects and business analysts. The goal is to develop a technical map of rules and data structures.

3. Physically:

This data model describes how the system is implemented with a particular DBMS system. This model is typically created by DBAs and developers. The purpose is to actually implement the database.

Concept model

The main goal of this model is to identify the entities, their attributes and their relationships. At this level of data modeling, hardly any details of the actual database structure are available.

The three basic tenants of Data Model are

Entity: A real thing

Attribute: characteristics or properties of an entity

Relationship: Dependency or association between two entities

For example:
Customer and product are two entities. The customer number and name are attributes of the Customer entity
Product name and price are attributes of the product unit
Sale is the relationship between the customer and the product

Properties of a conceptual data model

Provides organization-wide coverage of business concepts.
This type of data model was designed and developed for a business audience.
The concept model is developed independently of hardware specifications such as data storage capacity, location or software specifications such as DBMS providers and technology. The focus is on presenting data as a user sees it in the "real world".

Conceptual data models, known as domain models, create a common vocabulary for all stakeholders by defining basic concepts and scope.
Logical data model

Logical data models add further information to the conceptual model elements. It defines the structure of the data elements and defines the relationships between them.

The logical data model has the advantage that it forms the basis for the physical model. However, the modeling structure remains generic.

No primary or secondary key is defined at this data modeling level. At this level of data modeling, you need to review and adjust the connection details previously established for relationships.

Characteristics of a logical data model

Describes the data requirements for a single project, but can be integrated into other logical data models depending on the scope of the project.
Developed and developed independently of the DBMS.
Data attributes have data types with precise precision and length.
Normalization processes for the model are usually applied up to 3NF.

Physical data model

A physical data model describes the database-specific implementation of the data model. It provides an abstraction of the database and helps to generate a schema. This is due to the wealth of metadata that a Physical Data Model offers.

This type of data model also helps in visualizing the database structure. It helps in modeling database column keys, restrictions, indexes, triggers, and other RDBMS functions.
Features of a physical data model:

The physical data model describes the data requirements for a single project or application, but can be integrated into other physical data models depending on the scope of the project.
The data model contains relationships between tables that affect the cardinality and nullability of the relationships.
Designed for a specific version of a DBMS, location, data store, or technology to be used in the project.
Columns should have precise data types, assigned lengths, and default values.
Primary and foreign keys, views, indexes, access profiles and permissions, etc. are defined.

Advantages and disadvantages of the data model:

Advantages of the data model:

The main goal of any data model design is to ensure that the data objects offered by the functional team are accurately represented.
The data model should be detailed enough that it can be used to create the physical database.
The information in the data model can be used to define the relationship between tables, primary and foreign keys, and stored procedures.
The data model helps companies communicate within and between organizations.
The data model helps to document data assignments in the ETL process
Help identify the right data sources to populate the model

Disadvantages of the data model:

For the developer's data model, one should know which physical data characteristics are stored.
This is a navigation system that generates complex application development, management. Hence, it requires knowledge of the biographical truth.
Even minor changes in the structure have to be changed throughout the application.
There is no fixed language for data manipulation in DBMS.

Conclusion

Data modeling is the process of developing a data model for the data that is to be stored in a database.
Data models guarantee consistency in naming conventions, standard values, semantics and security while at the same time ensuring data quality.
The data model structure helps define the relational tables, the primary and foreign keys, and the stored procedures.
There are three types of conceptual, logical and physical.
The main goal of the conceptual model is to define the entities, their attributes and their relationships.
The logical data model defines the structure of the data elements and defines the relationships between them.
A physical data model describes the database-specific implementation of the data model.
The main goal of any data model design is to ensure that the data objects offered by the functional team are accurately represented.
The main disadvantage is that even minor changes in the structure require changes to the entire application.