Data Modeling Process PDF

203 Data Modeling Process The data modeling process involves a series of steps that guide the creation and refinement ** ** ** ** ** ** of a data model. ** ** * This...

203 Data Modeling Process The data modeling process involves a series of steps that guide the creation and refinement ** ** ** ** ** ** of a data model. ** ** * This structured approach ensures that the resulting model * *** meets the needs of the application, ** * * maintains data integrity, ** ** * * and optimizes database performance. ** ** * *** Steps in the Data Modeling Process *** *** ** ** ** ** *** ==* "Planning and Requirements Analysis" *== == 1 Requirements Gathering ** ** == * Work with stakeholders to gather information about the data needs of the application. ** ** * * Identify the * ** Entities, ** ** Data Flows, ** and Relationships ** **  * based on business requirements. * == 2 Define Entities and Relationships *** *** == * Determine the main entities ** *** (e.g., *** Customers, *** *** Orders, *** *** Products) *** * that will be part of the model. * * Define relationships between entities, such as ** ** * ** One-To-One, ** ** One-To-Many, ** or Many-To-Many. ** ** * == "Building a Data Model" == * == 3 Create the Conceptual Model ** ** * *== * Develop a high-level model that includes Entities and Relationships without diving ** ** ** ** ** ** into specific attributes. ~~ ~~ * * Review the model with stakeholders to confirm that it aligns with business goals. ** ** * == 4 Design the Logical Model ** ** * *== * Add detailed attributes for each entity, specifying Data Types and Keys. ** ** ** ** ** ** * * Establish * *** Primary Keys for unique identification ** * * and Foreign Keys to define relationships. ** ** * *** Normalize data to eliminate redundancy and improve data consistency. ** ~~ ~~ * == 5 Build the Physical Model ** ** * *== * Map the logical model to the physical structure of the chosen DBMS. ** ** * * Define * * specific Data Types, ** ** * *** Indexing, *** and Storage requirements. ** ** * Optimize for performance based on expected query patterns. * ==* "Validation and Testing *== == 6 Validate the Model ** ** ==  * Review the physical model to ensure it meets the application’s performance and ** scalability requirements. ** * * Conduct tests to confirm that the model can handle real-world data volume and ** ** ** queries efficiently. ** * ==* "Maintaining a Data Model" *== == 7 Implement and Refine ** ** == * Implement the model in the database, creating ** ** * ** Tables, ** ** Indexes, ** and Constraints. ** ** *** Monitor database performance and make adjustments as needed to maintain ** ** ** efficiency. * Common Considerations in the Data Modeling Process *~ Normalization vs. Denormalization: ~** *** * Balance between * * reducing redundancy (Normalization) ~~ ~~ ** ** * * and optimizing for query performance (Denormalization). ** ** ** ** * *~ Scalability: Design the model to support future growth in ~** ** ** ** * *** Data Volume *** * and Complexity. ** ** * *~ Data Security: Include considerations for Data Access and Privacy to protect sensitive ~ ** ** ** ** ** ** information. * *~ Data Integrity: Use Constraints and Relationships to maintain Accuracy and ~ ** ** ** ** ** ** ** Consistency across the model. ** *