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Database normalization is a critical process for designing and optimizing relational databases. By following a series of rules and best practices, normalization helps eliminate data redundancy, improve data integrity, and make databases more efficient and adaptable to future changes.

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Database normalization refers to the process of structuring a relational database in accordance with a series of "normal forms" in order to reduce data redundancy and improve data integrity. Normalization was first proposed by Edgar F. Codd in as part of his relational model for databases. The goal of normalization is to organize data into stable structures by progressively applying higher normal forms.

Each normal form builds upon the previous one and involves a series of tests on the database to certify whether it satisfies the requirements of that normal form. Typically, 3rd normal form is sufficient for most applications. Data Redundancy: Information is stored in multiple places, wasting space and increasing the likelihood of inconsistencies.

Normalization works by decomposition, grouping into separate relations those data elements that are dependent on the same key data elements.

For example, if customer addresses are stored with each order, they may become out of sync if a customer moves and their address is updated on some records but not others. Update Anomalies: Making changes to redundant data requires updating all instances, which is time-consuming and error-prone. If some instances are overlooked, it results in data inconsistency.

Insertion Anomalies: When some data depends on the presence of other data, it may not be possible to store that information unless the parent data is stored as well. Deletion Anomalies: Deleting some data may unintentionally remove other related data. By applying the rules of normalization and organizing data into separate but related tables, we eliminate many of these issues.

While a fully normalized database may take up more space than an unnormalized one, the benefits in terms of data integrity, consistency, and flexibility are well worth the tradeoff for most applications.