Which database design practice aims to minimize redundancy and ensure data integrity in a relational database?

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Multiple Choice

Which database design practice aims to minimize redundancy and ensure data integrity in a relational database?

Normalization is the practice of organizing a relational database to minimize redundancy and ensure data integrity by splitting data into related tables and defining clear keys. By separating data into logical tables—such as customers, orders, and order_items—and linking them with primary and foreign keys, updates, deletions, and insertions occur in one place, reducing the risk of inconsistent information spread across the database. This structure prevents update anomalies (where changes must be made in multiple places), insert anomalies (avoiding the need to store repetitive data to add a new item), and delete anomalies (avoiding losing related data unintentionally). For example, keep customer details in a separate table and reference customers from the orders table via a customer_id. If a customer’s address changes, you update it in one place, and all related orders reflect the correct information. Denormalization, by contrast, adds redundancy deliberately to improve read performance but reintroduces potential inconsistencies. De-duplication is a broader data-cleaning concept, not specific to relational design for guarding data integrity, and standardization focuses on consistent formats and naming rather than the structural organization that minimizes redundancy.

Normalization is the practice of organizing a relational database to minimize redundancy and ensure data integrity by splitting data into related tables and defining clear keys. By separating data into logical tables—such as customers, orders, and order_items—and linking them with primary and foreign keys, updates, deletions, and insertions occur in one place, reducing the risk of inconsistent information spread across the database. This structure prevents update anomalies (where changes must be made in multiple places), insert anomalies (avoiding the need to store repetitive data to add a new item), and delete anomalies (avoiding losing related data unintentionally).

For example, keep customer details in a separate table and reference customers from the orders table via a customer_id. If a customer’s address changes, you update it in one place, and all related orders reflect the correct information. Denormalization, by contrast, adds redundancy deliberately to improve read performance but reintroduces potential inconsistencies. De-duplication is a broader data-cleaning concept, not specific to relational design for guarding data integrity, and standardization focuses on consistent formats and naming rather than the structural organization that minimizes redundancy.

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