One article takes you through data models: conceptual model, logical model and physical model

Data models are the cornerstone of organizational data management and a key component in building information infrastructure. Data models provide organizations with a clear data structure and logical framework, making data management more efficient and sustainable. In the digital era, data has become one of the most valuable assets of an enterprise, and the design and implementation of data models determine the effectiveness and credibility of data in enterprise operations and decision-making. A good data model can not only simplify complex data landscapes and improve data quality and consistency, but also optimize database performance and support data analysis and decision-making. Therefore, the significance of data models is to provide data-driven decision support for enterprises and promote business innovation and competitiveness improvement. It provides data-driven decision support for enterprises and promotes business innovation and competitiveness improvement.

Data modeling is a complex and delicate process involving multiple levels of considerations and decisions. First, data modeling requires an in-depth understanding and analysis of business requirements to ensure that the established model accurately reflects business processes and data relationships. Secondly, data modeling needs to take into account the characteristics of different data types and sources, as well as the complex interactions between them, which requires the establishment of appropriate data structures and association rules. In addition, as the amount of data increases and business needs change, data modeling also needs to have a certain degree of flexibility and scalability to cope with future challenges and changes. Finally, data modeling involves the needs and opinions of multiple stakeholders and requires effective communication and coordination to reach consensus and gain support. To sum up, the complexity of data modeling is reflected in the in-depth understanding of business requirements, the design and optimization of data structures, and the communication and coordination of stakeholders. Various complex factors need to be comprehensively considered and processed in order to Establish effective data models.

Data model is a way of describing the abstract process of things in the real world. Conceptual data models, logical data models and physical data models are the three types of models of this abstraction. The conceptual model is used to capture business requirements and conceptual structures, the logical model defines the logical structure and relationships of data, and the physical model is the specific design of physical implementation based on the logical model. The data modeling process usually includes requirements analysis, conceptual modeling, logical modeling, physical modeling and verification. Each stage has specific tasks and technical tools that require detailed planning and execution. Mastering these concepts and processes can help enterprises better understand and manage their data assets and improve the efficiency and quality of data management.

What is a data model

A data model is a tool for simulating and abstracting real-world features from a data perspective. It extracts the main features of information based on business needs and maps business information (objects). relationship between them. Data models can not only simulate business scenarios more realistically, but are also solid records of important business models and rules. It includes three stages: conceptual model, logical model and physical model. From abstract objects at the conceptual level to solidified records at the physical level, it runs through the entire evolution process of the data model to ensure perfect alignment with business needs and technical constraints.

The data model is the core and foundation of the database system. Any database system must be built on a certain data model. However, due to the complexity of the real world, it is not feasible to build data models directly from the real world. First, it is necessary to abstract the real world into the information world and establish a data model in the information world, and then further transform the data model in the information world into a computer-implementable form to support the operation of the database system. When building a data model, you first need to abstract the real world into an information world and establish a data model in the information world. Then, the data model in the information world is further transformed into a computer-implementable form to support the operation of the database system. The establishment of data model is a key step in database system design. A good data model should accurately reflect the characteristics of the real world and provide efficient data storage and retrieval functions. Common data models include hierarchical models, network models, relational models, etc. All in all, the data model is the core and foundation of the database system. Any database system must be built on a certain data model. However, due to the

# of the real world

The data model is the representation of all business concepts and logical rules involved in the enterprise operation and management process. It is based on the expression of entities, attributes and their relationships, and has become a communication method between business personnel, IT personnel and developers. The bridge is the blueprint for data information in system construction. Data model design mainly includes conceptual model design, logical model design and physical model design. Concept model design describes the relationships and characteristics of business concepts through basic elements such as entities, attributes, and relationships. Logical model design is based on the conceptual model and transforms it into a data structure and association rules that can be recognized by the database. Physical model design is to transform the logical model into a physical structure that can be understood and operated by specific database software, including table partitions, indexes, etc. Data model design needs to consider the complexity and correlation of the business. During the design process, the requirements for data consistency, completeness and accuracy must be met. At the same time, it is also necessary to consider the system's reliability. The conceptual model is based on real-world relational semantics, abstracts data requirements into business objects and business processes, simplifies and expresses them as " Entity-Relationship (E-R) diagram. The logical model is further refined and standardized based on the conceptual model and is used to define the logical relationship between data. The physical model is the specific implementation of the logical model, describing the structure of the real database table, including tables, views, fields, data types, etc. The achievement of the physical model indicates that business processes and entity relationships have been solidified into table relationships in the database, which can be used, verified, processed, and maintained to form a complete data model.

There is a basic one-to-one mapping relationship between the physical model and the logical model. In the logical model, entities correspond to tables in the physical model, and attributes correspond to fields. The physical model is the physical implementation of the logical model on a specific database.

Characteristics of the conceptual model:

is a high-level data model that focuses on defining important business concepts and their interrelationships.

Contains core data entities or their collections, as well as business relationships between entities.

Features of the logical model:

Further decomposes and refines the conceptual data model.

Describes entities, attributes and the relationships between them.

Usually follow the "third normal form" when designing to reduce data redundancy.
• Features of the physical model:
• Describes the details of the model entities, balancing the relationship between data redundancy and performance.

You need to consider factors such as the database product used, field type, length, index, etc.

The database platform and application architecture must be determined.
• To a certain extent, the data model can be regarded as one of the most important results of data architecture, because it carries business requirements from natural language to data language conversion process. Through the data model, abstract business concepts and logical rules are clearly mapped into specific data entities, attributes, and relationships, providing a foundation and guidance for the design and implementation of database systems. The establishment of data models enables complex business processes and information needs to be clearly expressed and efficiently managed, providing reliable support for enterprise operations and decision-making. Therefore, the role of data model in data architecture cannot be ignored. It is a key part of building a reliable and efficient data management system.

Conceptual Model (CDM)

The core task of conceptual model (Conceptual data models, CDM) is to synthesize and summarize various conceptual entities in the business domain. The focus of this process is on analyzing conceptual entities and their interrelationships rather than describing in detail the various attributes of individual conceptual entities. By using conceptual entities as clues, the requirements analysis results are reviewed, the scope of modeling is determined, the modeling topics are divided, the main business relationships are sorted out, and the framework of the logical data model is constructed.

The conceptual data model is a structured view of the business used to support business processes, record business events, and track relevant performance indicators. This model focuses on identifying the data used in the business rather than its processing or physical characteristics. The model's perspective is independent of any underlying business applications.

The conceptual data model represents the overall structure of the data required to support business requirements, independent of any software or data storage structure. Its features include:

• Overall view of data structure in business context.
• Does not rely on any database or physical storage structure.
• Objects that may never be implemented in a physical database. Some concepts and processes may not appear in the model, but they are important for the company to understand and explain its business.
• Support data required to execute business processes or enterprise operations.

Conceptual data models are tools for business and IT to define:

• The scope of data requirements.
• Business terms and metrics adopted across different business units and across the enterprise.
• Business Data Objects (BDO) and their relationships.

The following is an example of a conceptual model:

Logical Model (LDM)

Logical Data Models, LDM) is a further refinement of the conceptual data model, aiming to clarify the attributes, relationships and constraints of data entities. According to the data standard, it clarifies the Chinese and English names of entities, the data types and precision of attributes, and defines primary keys, unique indexes, and relationships between entities. When designing, it follows the third paradigm to reduce data redundancy and is a tool for communication between business and technical personnel.

In logical data modeling, the team describes data elements and the relationships between them in detail to ensure that data elements are clearly defined and relationships are accurately established. During the design process, by introducing context and details, we help the team better understand data requirements and provide guidance for business process improvement and application design. This structured approach provides a foundation for database design, helping to reduce costs, increase efficiency, and promote data reuse and future model building.

Logical data modeling provides organizations with a comprehensive view of data to help understand and meet business needs. It not only provides a foundation for current application design, but also lays the foundation for future data models and system architecture, supporting the long-term growth of the organization. By accurately defining data structures and relationships, logical models facilitate communication and collaboration within an organization, laying the foundation for effective database design and application development.

The following is an example of a logical model, which is a refinement of the above figure (conceptual model):

Physical Model (PDM)

Physical data models (PDM) is the last stage of the data modeling process. It transforms concepts such as entities, attributes, and relationships in the logical model (LDM) into models suitable for a specific database management system (DBMS). Specific design. It represents the physical implementation of the original business requirements and logical design.

The physical data model describes the structure of the database and defines how data is stored, organized, and accessed at the physical level. It provides a detailed view of the database schema, specifying tables, columns, data types, relationships, indexes, constraints, etc. Therefore, the Physical Data Model (PDM) serves as a framework that guides developers in creating and optimizing actual databases, taking into account storage, performance improvements, and the specific characteristics of the database management system (DBMS).

Design Notes:

1. Improve data storage efficiency: By specifying the structure of tables, data types and lengths, increase the efficiency of data storage, ensure reasonable use of database space, and reduce unnecessary overhead.
2. Improve performance: By strategically implementing indexes, partitioning large tables, and clustering related data, the physical data model improves database performance, speeds up data retrieval, and improves system responsiveness.
3. Enforce data integrity: The physical data model enforces integrity constraints such as primary keys, unique constraints, and foreign keys. These constraints help ensure the accuracy and reliability of the data stored in the database and prevent anomalies from occurring.
4. Supports scalability: The physical data model allows efficient partitioning of large tables, allowing the database to handle growing data volumes while maintaining performance. It simplifies data management and scales as your data needs increase.
5. Optimize queries: By using strategic indexes and carefully designed selections, the physical data model accelerates data retrieval and improves the efficiency of database queries.
6. Enhance database design communication: The physical data model serves as a visual aid to help developers, administrators, and business users understand database design more easily. It promotes effective communication and a shared understanding of how data is structured and organized.
7. Reduce Redundancy: The normalization technique employed in the physical data model reduces redundancy by splitting tables into smaller, related components. This results in simplified and consistent data representation in the database.
8. Maximize resource efficiency: Since the physical data model is consistent with the specific features and functions of the selected technology stack, it ensures optimal utilization of system resources in the DBMS environment.

The following figure is the ER diagram of the physical model, which is basically the same as the ER diagram of the logical model.

Data model and metadata (metadata)

Metadata (metadata) is information about the organization of data, data domains and their relationships. In short, metadata is data that describes data, covering the content, structure, use and management of data. It provides background information and context for the data, helping to understand its meaning and purpose. According to different application fields or functions, metadata can generally be divided into three categories: business metadata, technical metadata and operational metadata.

A data model is an abstract description of data organization and representation, describing the relationships, attributes, and constraints between data. The data model defines the structure and logic of the data, as well as how the data is operated and processed in the system.

Conceptually, the data model is an integral part of metadata. In practical applications, the description content of the conceptual model can be understood as part of business metadata, because it mainly focuses on the definition of business concepts and logical rules, helping business personnel understand the meaning of data and business processes. The description content of the logical model and physical model can be regarded as part of the technical metadata. The logical model defines the relationships and constraints between data, while the physical model describes how data is stored and physically structured in the database. This information is crucial to database developers and system administrators. In addition, operational metadata describes the operational attributes of data, such as data modification, deletion, and access permissions. This information is critical to data management and maintenance of security. Therefore, data models play a key role in the entire data life cycle. As part of metadata, they together form the basis for data management and utilization.

Data models and metadata systems play different roles at different stages of the data life cycle. The data model focuses more on the information system design and development stage, used to capture and define business requirements, design system architecture, and guide database design and application development. It provides an abstract, structured view that describes the relationships, constraints, and processes between data, helps business personnel and technical personnel understand the meaning and purpose of data, and guides system design and development.

In contrast, the metadata system pays more attention to the data consumption stages such as data governance and data application. At this stage, metadata systems are used to manage and maintain data assets and support data collection, storage, sharing, and analysis. It records and manages information such as the attributes, structure, location and usage of data, provides background information and context of the data, helps managers understand the source, quality, security and other aspects of the data, and supports data retrieval, analysis and Reporting to promote value maximization and compliance management of data assets.

The data model is mainly used in the information system design phase, while the metadata system focuses more on the data governance and data management phases. The two complement each other and jointly support the effective management and utilization of data.

Data model and metadata model (metamodel)

The data model describes the core business entities and their associated relationships, definitions and business rules. But metadata models (metamodels) are often confusing. A metamodel is a data model of metadata that describes core metadata objects and their relationships and associated business rules.

The metadata model (metamodel, metamodel) is a data model that describes metadata. Its main task is to describe core metadata objects and their relationships and associated business rules. In business intelligence (BI) and online analytical processing (OLAP), data governance (DG) platforms, metadata model (metadata model) is a descriptive data structure abstraction layer used to transform technical data structures into user-friendly Structure. It provides a detailed description of the data structure and its representation, ensuring that the data is prepared in a user-friendly manner, including the data itself, the names of the data elements and the visible structure.

In business intelligence (BI), online analytical processing (OLAP), and data governance (DG) platforms, metamodels are one of the basic components, similar to data models that persist and query data in applications. basic role. It supports storage and query functions of metadata, and the design is driven by DG use cases as well as operational metadata.

An example of a metadata model (metamodel, metamodel) is as follows:

The data model is the cornerstone of organizational data management and the basis for building information infrastructure. Key components. It provides a clear data structure and logical framework, making data management more efficient and sustainable. Data models are divided into three types: conceptual model, logical model and physical model, with different stages from abstract to concrete, ensuring perfect alignment with business needs and technical constraints. Correspondingly, the metadata model is a data model that describes metadata, and its main task is to describe core metadata objects and their relationships and associated business rules. The metadata model is a basic component of the data governance platform. It is similar to the role of the data model in the application and supports the storage and query functions of metadata.

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