Data Modeling for Data Warehouse: A Practical Guide with Examples
Data Modeling Concepts in Data Warehouse: A Comprehensive Guide
Data modeling is the process of designing a framework that defines the data relationships within a database or a data warehouse. It involves creating a visual schema to describe associations and constraints between datasets. The goal of data warehouse modeling is to develop a schema describing the reality, or at least a part of the reality, which the data warehouse is needed to support.
Data Modeling Concepts In Data Warehouse Pdf Free
Data modeling is an essential stage of building a data warehouse because it is necessary to first map out the warehouse formats and structure in order to determine how to manipulate each incoming data set to conform to the needs of the warehouse design. The data model is then an important enabler for analytical tools, executive information systems (dashboards), data mining, and integration with any and all data systems and applications.
Data modeling has many benefits for data warehouse, such as:
It helps to understand the business requirements and objectives of the data warehouse.
It facilitates communication and collaboration among stakeholders, developers, and users.
It improves the performance, scalability, security, and maintainability of the data warehouse.
It ensures data quality, consistency, and integrity across the data warehouse.
It supports decision-making and problem-solving by providing accurate and reliable information.
Data models can be classified into three categories, which vary according to their degree of abstraction. The process will start with a conceptual model, progress to a logical model and conclude with a physical model. Each type of data model is discussed in more detail below:
Conceptual Data Model
A conceptual data model is also referred to as a domain model and offers a big-picture view of what the system will contain, how it will be organized, and which business rules are involved. Conceptual models are usually created as part of the process of gathering initial project requirements.
Typically, they include entity classes (defining the types of things that are important for the business to represent in the data model), their characteristics and constraints, the relationships between them and relevant security and data integrity requirements. Any notation is typically simple.
To create a conceptual data model for data warehouse, the following steps can be followed:
Identify the main entities and their attributes that are relevant for the data warehouse.
Define the relationships and cardinalities among the entities.
Specify the business rules and constraints that apply to the entities and relationships.
Validate the conceptual data model with the stakeholders and users.
The advantages of conceptual data model are:
It provides a high-level overview of the data warehouse scope and purpose.
It is easy to understand and communicate with non-technical audiences.
It is independent of any specific technology or implementation details.
The disadvantages of conceptual data model are:
It does not provide enough detail for physical implementation or data manipulation.
It may not capture all the complexities and nuances of the real-world data.
It may require frequent revisions as the project requirements change or evolve.
Logical Data Model
A logical data model is less abstract and provides greater detail about the concepts and relationships in the domain under consideration. Logical models are usually derived from conceptual models by adding more specifications and refinements, such as data types, primary keys, foreign keys, normalization, and denormalization.
A logical data model represents how the data should be structured and organized in a logical manner, without regard to how it will be physically implemented or stored. It also defines the business logic and rules that govern the data and its operations.
To create a logical data model for data warehouse, the following steps can be followed:
Convert the entities and attributes from the conceptual data model into tables and columns.
Determine the primary keys and foreign keys for each table.
Normalize or denormalize the tables according to the data warehouse design methodology (such as star schema, snowflake schema, or galaxy schema).
Add indexes, views, and other logical objects to optimize the data warehouse performance and functionality.
Validate the logical data model with the developers and users.
The advantages of logical data model are:
It provides a detailed and precise representation of the data warehouse structure and behavior.