Normalization vs Denormalization in RDBMS | Examples | Tables | SQL Query | codestorywithMIK
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In this video we will try to explain Normalization vs Denormalization in RDBMS in the simplest way possible.
Topic Name : Normalization vs Denormalization in RDBMS | Examples | Tables | SQL Query | codestorywithMIK
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Summary : Normalization and denormalization are two concepts in the context of relational database management systems (RDBMS) that deal with the organization and structure of data in tables. They have opposite goals and are used in different situations to achieve specific objectives.
### Normalization:
Normalization is the process of organizing data in a database to reduce redundancy and dependency by organizing fields and table of a database. The primary goals of normalization are:
1. **Minimizing Redundancy:** Normalization helps in minimizing data redundancy by dividing large tables into smaller tables and defining relationships between them.
2. **Avoiding Update Anomalies:** It helps to prevent update anomalies by ensuring that data is stored in a way that allows for efficient updates without introducing inconsistencies.
3. **Improving Data Integrity:** Normalization improves data integrity by reducing the chances of inconsistencies in the database.
The normalization process involves decomposing tables into smaller ones and establishing relationships between them using primary and foreign keys. Common normalization forms include 1NF (First Normal Form), 2NF (Second Normal Form), 3NF (Third Normal Form), and so on.
### Denormalization:
Denormalization is the process of deliberately introducing redundancy into a database by combining tables that have been normalized. The primary goals of denormalization are:
1. **Improving Query Performance:** Denormalization can improve query performance by reducing the need for joins and aggregations. This is particularly useful in situations where read operations are more frequent than write operations.
2. **Simplifying Query Complexity:** By storing redundant data, queries may become simpler and more straightforward, as there is no need for complex joins across multiple tables.
3. **Supporting Specific Use Cases:** Denormalization is often applied in situations where certain types of queries are more common, and the database design is optimized for those specific use cases.
It's important to note that denormalization introduces trade-offs, such as increased storage space and potential data inconsistency if updates are not carefully managed. It is typically considered in situations where the performance gains outweigh these trade-offs and where data integrity can still be maintained.
In summary, normalization is concerned with reducing redundancy and maintaining data integrity, while denormalization is focused on improving query performance and addressing specific use cases. The choice between normalization and denormalization depends on the specific requirements and characteristics of the application and the types of queries it needs to support.
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✨ Timelines✨
00:00 - Introduction
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