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What is the difference between logical data model and conceptual data model?
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Physical data models are used to visualize the physical structure of databases and data files. Logical data models are used to visualize data entities, attributes, keys, and relationships.
The conceptual data model is a structured business view of the data required to support business processes, record business events, and track related performance measures. This model focuses on identifying the data used in the business but not its processing flow or physical characteristics.
Logical data modeling also functions to detail the attributes associated with a data element. For example, a logical data model would specify the nature of a data element, i.e., account name (string), account number (integer).
Chapter 4: Subatomic Particles. Physical models replicate an object on a different scale, while conceptual models describes the behavior of a system.
Additionally, the conceptual data model is the basis for developing the logical data model, while the logical data model is the basis for developing the physical data model. Also, the conceptual data models are simpler than the logical data models. In brief, data modelling is the process of recognizing the data that has to be stored in a database.
There are various types of data models, and two of them are conceptual and logical data model. 1. What is Conceptual Data Model 2. What is Logical Data Model 3. Difference Between Conceptual and Logical Data Model
Conceptual data models are usually very simple, but sometimes the data structure diagrams used to represent them also include basic definitions of entities’ attributes, as shown below: A logical data model is probably the most-used data model.
The three levels of data modeling, conceptual data model, logical data model, and physical data model, were discussed in prior sections. Here we compare these three types of data models. The table below compares the different features: Below we show the conceptual, logical, and physical versions of a single data model.
In the conceptual data model you worry only about the high level design - what tables should exist and the connections between them. In this phase you recognize entities in your model and the relationships between them.
The logical model comes after the conceptual modeling when you explicitly define what the columns in each table are. While writing the logical model, you might also take into consideration the actual database system you're designing for, but only if it affects the design (i.e., if there are no triggers you might want to remove some redundancy column etc.)
There is also physical model which elaborates on the logical model and assigns each column with it's type/length etc.
Here is a good table and picture that describes each of the three levels.
|----------------------|------------|---------|----------| | Feature | Conceptual | Logical | Physical | |----------------------|------------|---------|----------| | Entity Names | X | X | | | Entity Relationships | X | X | | | Attributes | | X | | | Primary Keys | | X | X | | Foreign Keys | | X | X | | Table Names | | | X | | Column Names | | | X | | Column Data Types | | | X | |----------------------|------------|---------|----------| 
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