g2) Data Normalization and Denormalization

Normalization:

Organizing data in a database to minimize redundancy and ensure data integrity. It involves decomposing a database into multiple tables.

1️⃣ Reduced data redundancy: By breaking data into separate tables and eliminating duplicate information, normalization helps to reduce data redundancy and ensures consistency.

2️⃣ Improved data integrity: Normalization helps enforce data integrity rules through the use of primary keys, foreign keys, and relationships between tables.

3️⃣ Improved query efficiency: Normalization can simplify querying and analysis by breaking down complex data into smaller, manageable tables.

Denormalization:

Intentionally introducing redundancy into a database design. It involves combining tables or duplicating data to improve query performance and simplify data retrieval.

1️⃣ Improved query performance: Denormalization can enhance query performance by reducing the number of joins and table lookups needed to retrieve data.

2️⃣ Simplified data retrieval: By combining related data into a single table, denormalization can simplify data retrieval and eliminate the need for complex join operations.

3️⃣ Reduced complexity: Denormalized databases often have a simpler structure, making them easier to understand and manage.

数据规范化和反规范化

规范化：

在数据库中组织数据以最小化冗余并确保数据完整性。它涉及将数据库分解为多个表。

1️⃣减少数据冗余：通过将数据打散到单独的表中并消除重复信息，规范化有助于减少数据冗余并确保一致性。

2️⃣ 改进数据完整性：规范化有助于通过使用主键、外键和表之间的关系来执行数据完整性规则。

3️⃣ 提高查询效率：规范化可以通过将复杂的数据分解成更小的、易于管理的表来简化查询和分析。

反规范化：

故意在数据库设计中引入冗余。它涉及组合表或复制数据以提高查询性能并简化数据检索。

1️⃣ 提高查询性能：非规范化可以通过减少检索数据所需的连接和表查找次数来提高查询性能。

2️⃣ 简化数据检索：通过将相关数据组合到一个表中，非规范化可以简化数据检索并消除复杂连接操作的需要。

3️⃣ 降低复杂性：非规范化数据库通常具有更简单的结构，使其更易于理解和管理。

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