Logical Data Modeling - Data Model

1 - About

A data model is an (abstract) model that describes how data are represented and accessed.

Data models formally define entity and relationships among entities for a domain of interest.

This is schema but on an entity/relationship level and not on raw data.

A data model is a representation of:

A data model is a formal way to represent the business rules.

It is built and modified until it represents the business well enough to write a system. Data models must be built during requirements.

A Data Model has three main components:

3 - Model

4 - Without Data Model

5 - Data Modeling Process

5.1 - Logical

5.2 - Physical data model

Logical design is what you draw with a pen and paper or design with a data modeling tools before building your data warehouse database. Physical design is the creation :

During the physical design process, you convert the data gathered during the logical design phase into a description of the physical structure. Physical design decisions are mainly driven by:

  • query performance
  • and database maintenance aspects.

For example, choosing a partitioning strategy that meets common query requirements enables the Database to take advantage of partition pruning, a way of narrowing a search before performing it.

A Physical Data Model is the physical manifestation of the logical data model (relationships) into database tables and foreign key constraints.

A physical DB schema describes the storage structures and access methods used in order to effectively access and maintain data.

5.3 - Difference between logical and physical

Both, the logical and physical data model are presented as ER diagrams. A logical model is slightly more abstract than a physical model.

  • A physical model represents exactly what is implemented in a database (table, column names, schema, indexes, data types, contraints, tablespaces, partitions and all the other requirements to define the database.
  • A logical model shows entities, attributes, keys and relationships. Names are usually more free-form and descriptive.

6 - Data Model Fit-Gap Analysis

Fit-gap analysis is where you compare your information needs and business requirements with the actual data model structure.

7 - Documentation / Reference

  • Source: Batini, C., Ceri, S., and Navathe, S. B., Conceptual Database Design: An Entity-Relationship Approach, The Benjamin/Cummings Publishing Company, Inc., 1992.

Data Science
Data Analysis
Data Science
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