Introduction
The process of developing the schemas for the data warehouse's detailed and summarized information is known as data warehouse modeling. The purpose of data warehouse modeling is to create a schema that describes the reality, or at least a portion of the reality, that the data warehouse must support.
Data warehouse modeling is essential in building a data warehouse for two essential reasons.
The first reason is to visualize the relationships among the warehouse data, and the second reason is to optimize the schema to make a well-structured data warehouse.
Also See, Multiple Granularity in DBMS and Checkpoint in DBMS
Data Modeling
The process of developing a visual representation of an entire information system or sections to express connections between data points and structures is known as data modeling.
Must Read, Super Keys in DBMS
Why is data modeling important?
Data modeling ensures that all data objects required by the Database are correctly represented. Omission of data will lead to inaccurate reports and produce incorrect results. The omission of data will result in incorrect reporting and bad outcomes. Data modeling aims to illustrate the types of data utilized and stored within the system, the relationships between them, how they can be grouped and organized, and their formats and attributes.
The Data Model gives a clear picture of business requirements.
The life cycle of Data Modeling
- Gathering Business Requirements
- Conceptual Data Modeling
- Logical Data Modeling
- Physical Data Modeling
- Development of Schema / the database
- Maintenance of data Model time to time as per requirement
Types of the data model
Data modeling facilitates the creation of a conceptual model and the establishment of relationships between items.
- Conceptual Data Model
- Logical Data Model
- Physical Data Model
Conceptual Data Model
Conceptual models are usually built as part of gathering early project requirements.
The conceptual model defines what the system contains.
This data model focuses on finding the data used in a business instead of the processing flow.
The main objective of this data model is to organize and establish business rules and concepts.
It includes entity classes, properties and constraints, relationships, and the necessary security and data integrity requirements.
- Its primary purpose is to establish entities, attributes, and relationships between two entities.
- Business stakeholders or data architects create it
- Its purpose is to create various business rules.
Logical Data Model
The logical Data Model map of rules and Data Structures includes the required data, such as tables, columns, etc.
A logical data model consists of tables, documents, descriptions, etc. The document structures are defined in this model.
This data model is always present in the root package object.
This type of data model helps create the physical model base. There is no secondary or primary key defined in this model.
- It defines the structure of data elements and their relationships also.
- Business analysts and data architects create it.
Physical Data Model
In a physical data model, we care about how the system can store the actual data.
It manages the replication, shards, etc., physically.
It defines the components and services which are required to build a database. It is created by using the database language and queries.
The physical data model provides database column keys, constraints, and RDBMS features.
- We create various schemas, abstraction of schemas, and different mapping types in these data models.
- Database administrators and developers create it.
- It is the actual implementation of the Database.
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