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In SQL interviews, one of the most frequently asked questions is about data normalization. Data normalization involves reorganizing data across multiple tables to minimize data redundancies and duplicates. The process helps in establishing logical relationships between data tables. For example, on platforms like Amazon, user information such as ID, name, email, and phone number is stored in one table, while purchase details and shipping information are organized into separate tables. The essence of normalization is to efficiently manage and connect data across different tables.
In SQL interviews, understanding data normalization is crucial. Itβs all about ensuring that the data in your database is organized efficiently to reduce redundancies and improve data integrity. Instead of lumping everything into a single table, data is spread across multiple tables in a structured way.
Imagine browsing an online store like Amazon. Your personal information is stored separately from your product purchases. This method of data organization is called normalization. It helps store data logically and efficiently, making data management simpler and more effective.
The ultimate goal of normalization is to reduce data duplication and ensure a clean database structure. By doing so, you connect different but related pieces of data, creating a comprehensive data network that is both accessible and maintainable. This is why SQL professionals are often probed about normalization in interviews.