Data 11 Data modeling progresses through distinct stages as organizations transform their business requirements into actual database implementations. The process begins with the conceptual data model, which serves as a foundational framework outlining the database at a high level and facilitating understanding of the database requirements among stakeholders. Logical data models serve as the foundation, providing a detailed visual representation of an organization’s data structures and relationships. Created by business analysts and data architects, these models define business rules , data entities, and their relationships using entity-relationship diagrams (ERDs). They focus on essential elements that represent business concepts without considering specific database implementation details.
Serves as a foundational framework outlining the database at a high level. Facilitates understanding of database requirements among stakeholders. Focuses on high-level business concepts and relationships.
Provides detailed visual representation of data structures and relationships. Defines business rules, data entities, and relationships using ERDs. Created by business analysts and data architects.
Transforms logical model into concrete database schema. Defines technical elements like storage requirements, keys, and constraints. Led by DBAs working with architects and developers.
Physical data modeling then transforms this logical model into a concrete database schema optimized for a particular database management system. During this transformation, database designers must consider several key factors: the specific relational database platform, hardware environment, data access frequency, and access paths. Physical data models define additional technical elements such as data storage requirements, primary and foreign keys, stored procedures, and data constraints. They may also introduce alterations to logical structures to improve data integrity, enhance performance, or optimize data organization.
While the logical model maintains an abstract representation focused on business requirements, the physical model specifies the actual database tables and implementation details. Database administrators (DBAs) typically lead this transformation process, working closely with data architects, business analysts, and application developers to ensure the physical design supports both technical efficiency and business needs. However, any deviation from the logical model requires careful consideration, as it can impact data quality and maintenance. When significant performance or usability improvements can be achieved, physical model modifications should be evaluated against potential drawbacks.
The following real-world example demonstrates how one organization navigated the transformation from logical to physical data models, making strategic modifications to improve performance, enhance usability, and optimize storage while maintaining essential business functionality.
What is Physical Data Modeling? Definition of Physical Data Modeling Physical data modeling is the process of creating a detailed, technical representation of a database schema, including the physical storage and data retrieval mechanisms. It involves translating the logical data model into a physical implementation, taking into account the specific database management system (DBMS) and storage technology that will be used. Unlike logical data models , which focus on business requirements and abstract data structures, physical data models define the actual database tables, columns, data types, and indexes. This transformation is crucial for ensuring that the database is optimized for performance, scalability, and data integrity.
Role of a Physical Data Model Physical data modeling is essential for ensuring that the database is designed to meet the performance, scalability, and security requirements of the application. A well-designed physical data model can improve data retrieval efficiency, reduce data redundancy, and enhance data security. It also helps to ensure that the database is optimized for the specific DBMS and storage technology being used, which can improve overall system performance. By carefully planning the physical data model, organizations can avoid potential issues related to data integrity, storage inefficiencies, and performance bottlenecks, ultimately leading to a more robust and reliable database system.
Importance of Physical Data Modeling Physical data modeling is essential for ensuring that the database is designed to meet the performance, scalability, and security requirements of the application. A well-designed physical data model can improve data retrieval efficiency, reduce data redundancy, and enhance data security. It also helps to ensure that the database is optimized for the specific DBMS and storage technology being used, which can improve overall system performance. By carefully planning the physical data model, organizations can avoid potential issues related to data integrity, storage inefficiencies, and performance bottlenecks, ultimately leading to a more robust and reliable database system.
From Logical to Physical: A Critical Transition The data modeling process begins with conceptual data models, which serve as initial frameworks to establish a common vocabulary among stakeholders by defining essential business concepts and objectives necessary for database design. Following this, the logical stages involve business stakeholders and data architects collaborating to define the foundation of the database structure. Logical data models define representations of real-world objects, capturing all data elements and relationships critical to business operations. These models incorporate:
Core Components Enterprise-wide scope:Complete coverage of business processes. Integration with existing information systems. Alignment with business intelligence needs. Structural elements:Detailed entity definitions. Attribute specifications. Relationship mappings. Data types and validation rules. Business rules implementation:Access constraints. Data validation parameters. Operational policies. Security requirements. Transformation Challenges The transition from logical to physical models requires careful planning, as modifications become increasingly complex after data has been loaded into the physical database. It is crucial to maintain the data structure during this transformation to ensure the organization of data elements and their interconnections align with business requirements. Database designers must consider several critical factors:
Structural Integrity:Maintaining conceptual data relationships. Preserving business rule enforcement. Managing data redundancy. Implementation Constraints:SQL server limitations. Storage optimization requirements. Performance considerations. Future Adaptability:Flexibility for business growth. Scalability of the database system. Maintenance requirements. This balance between theoretical purity and practical implementation becomes evident in the following case study, which demonstrates how one organization navigated these challenges effectively.
Diagram 1 Next, we need to know where to ship the product and send the bill each month. In this case, a customer can have more than one product line so we need to store the address at the customer level not the account level. This will enable us to share the address across multiple customer accounts. To complicate things, we also allow customers to use more than one address. Maybe Tom Smith wants the Elvis Velvet Paintings sent to his house but wants to hide his obsession with soap opera statuettes from his wife so he has those items sent to his office. We will then need to store the address at the customer level but point the account table to the correct address. We could do this by adding an address id to the account table. (see diagram 2)
Diagram 2 Let’s complicate things a little further (remember this is based on a real life example). We also need to allow a customer to send the shipments to one of his addresses and the bill’s to another. This will require a junction table between account and address. Using a junction table will enable: 1) a customer to have many addresses; 2) a customer to have many accounts; 3) an account to use many of the customers’ addresses.
In reality, the account may use two of the customer’s addresses. In the junction table we will add an address use code. The valid values would be 1=billing, 2=shipping. (see diagram 3)
Diagram 3 One more complication: Customer Tom wants to send the Three Stooges picture plates to his Grandmother but have the bills sent to him. Doing this would require us to establish Grandma as a customer and point the shipments to her. However, we can’t use the junction between account and address because that would point us to the wrong customer ID. Therefore, we need to create an optional alternate customer table. We would only need to insert to this table if the shipments are going to a different customer then the bills. This table must contain the account number, Grandma’s customer ID and Grandma’s address pointer. (see diagram 4)
Diagram 4 Pretty complicated to ship Elvis velvet paintings, isn’t it? That is what we thought. Even if performance was not an issue, the complications involved in maintaining and accessing these tables was not acceptable. We agreed that diagram 4 represented our logical needs but we needed to create something more usable physically. The most obvious change was getting rid of the junction between account and address. We only have two usages of address (billing and shipping). Therefore, we decided to add two columns to the account table in the physical data model: bill address ID and ship address ID. If the customer was sending the bills and the shipments to the same address they could contain the same ID. (see diagram 5)
The biggest drawback of this design is that it limits the number of alternate customers to one and the number of addresses for an account to two. We discussed this with the business and agreed that it would be impractical to have more than one customer at a time designated as the recipient of the shipments.
Conclusion To summarize, compare diagram 6 with diagram 4.We managed to improve performance, improve usability, and reduce DASD. While we did violate some pure modeling rules we provided a faster, cheaper, and more usable database to the business. It was important to go through this entire process when performing logical to physical data model transformation. If you jump right to the physical model without truly understanding the logical model, it becomes easy to make mistakes and incorrect assumptions. Keep in mind the words of Albert Einstein , “Everything should be made as simple as possible, but not simpler.”
