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In order to ensure optimal database performance, the logical and physical design should consider the user requirements thoroughly. Suppose you have been hired to transform a conceptual model into a logical model for a sales database.
Describe the specific steps that you must perform in order to appropriately construct the database model.
Speculate the risks that might present themselves for each step mentioned, and how you would avoid or mitigate those risks.
Establish Relationships and Foreign Keys: Identify the relationships between tables (e.g., one-to-many, many-to-many). For a one-to-many relationship, a foreign key (FK) is added to the table on the "many" side. For example, to link Order to Customer, the CustomerID (the primary key of the Customer table) would be added as a foreign key to the Order table. For many-to-many relationships (e.g., Product and Order), an intermediate "junction" table is created (e.g., Order_Items), which contains foreign keys from both tables.
Normalize the Tables: Apply normalization rules (e.g., 1NF, 2NF, 3NF) to minimize data redundancy and improve data integrity. Normalization breaks down tables to ensure each column depends only on the primary key, thereby reducing anomalies during data insertion, updates, and deletions. For example, if a table for Orders also contained CustomerAddress, a 3NF rule would move CustomerAddress to the Customer table to avoid repeating the address for every order.
Refine and Document the Model: Review the entire logical model for accuracy and completeness. Add detailed documentation, including data types for each column (e.g., VARCHAR, INT, DATE), constraints (e.g., NOT NULL, UNIQUE), and business rules. This step prepares the model for the physical design phase.
Step 1: Translating Entities and Attributes
Risk: Missing or misinterpreting key user requirements, leading to an incomplete model. For example, a "Product" entity might be missing an attribute for "Product Weight," which is critical for shipping calculations.
Mitigation: Conduct thorough interviews and workshops with stakeholders. Use an iterative process, presenting the model at each stage for validation and feedback from the business users.
Step 2: Defining Primary Keys
Risk: Choosing a primary key that is not truly unique, such as a customer's name, or a key that changes over time (e.g., an employee's department number), which would break relationships and cause data inconsistencies.
Mitigation: Use a unique, stable, and simple identifier, often a system-generated surrogate key (e.g., an auto-incrementing integer), which has no business meaning but is guaranteed to be unique.
Step 3: Establishing Relationships and Foreign Keys
Risk: Incorrectly defining relationships (e.g., using a one-to-many for a many-to-many relationship), resulting in a model that cannot accurately represent business processes.
Mitigation: Create an entity-relationship diagram (ERD) and review it with business analysts to confirm that the relationships accurately reflect real-world business rules. For example, check if a single order can contain multiple products, or if a single product can be on multiple orders.
Step 4: Normalizing the Tables
Risk: Over-normalization, which can create too many tables and lead to complex queries and decreased performance. While normalization is good for data integrity, it can sometimes be a trade-off for speed.
Mitigation: Understand that the goal is optimal performance, not perfect normalization. After reaching a level like 3NF, evaluate the most frequent queries and, if necessary, consider a controlled level of denormalization for specific performance-critical tables, while documenting the decision and its implications.
Step 5: Refining and Documenting the Model
Risk: Incomplete or unclear documentation, which can lead to misinterpretation during the physical design and implementation phases. This is a primary cause of misalignment between the designed model and the final product.
Mitigation: Establish a standard for documentation. Use data dictionary tools to define each column, its data type, and its purpose. Include business rules and assumptions. This ensures that the logical model is a complete and unambiguous blueprint for the next stages of development.
To transform a conceptual model into a logical model for a sales database, you must perform a series of structured steps, ensuring user requirements are at the forefront of the process.
Translate Entities and Attributes: Convert each high-level entity from the conceptual model (e.g., Customer, Product, Order) into a table. Each attribute of the entity becomes a column in that table. For instance, the "Customer" entity would become the Customer table with columns like CustomerID, FirstName, LastName, and Email.
Define Primary Keys: Assign a unique identifier, or primary key (PK), to each table. This key uniquely identifies each row and ensures data integrity. For the Customer table, CustomerID would be the primary key.