If you work with databases, you probably have heard of normalization. But what is it and why is it important in SQL? In this article, you will learn the basics of normalization, the benefits and drawbacks of applying it, and some common types and examples of normalization in SQL.
Normalization is a process of designing and organizing database tables to reduce data redundancy and improve data integrity. Redundancy means having the same data stored in multiple places, which can lead to inconsistency and waste of space. Integrity means ensuring that the data is accurate, consistent, and follows certain rules and constraints. Normalization involves splitting large tables into smaller ones, eliminating duplicate columns, and creating relationships between tables using foreign keys and primary keys.
Normalization is a process in database design that involves organizing the data in a relational database to reduce redundancy and improve data integrity. The main goal of normalization is to structure the database in such a way that it minimizes data redundancy and ensures that data is stored in a logically consistent manner.
Normalization is a database design rules that organizes data in a relational database to reduce data redundancy and dependency. The primary goal of normalization is to structure the database in such a way that it minimizes data anomalies and ensures data integrity. It involves breaking down large tables into smaller, related tables and defining relationships between them.
Normalization is a design technique that breaks the table into smaller ones and uses Referential integrity concept to keep the broken tables connected. This reduces the data redundancy and improves consistency but comes at the cost of expensive access due to join operations. When you need to extract the data from two/more tables, you need to perform a JOIN which is a costly operation. There must be a balance between the level of embedding (when the data is stored together) and referencing(when the data is normalized and connected through references). Normalization also depends on the DB architecture. RDBMS usually implements this where as MongoDB which is No-SQL works on the concept of Data that is accessed together must be stored together.
In an RDBMS normalization is a 2 step process aimed at optimizing the database for effective querying and ensuring data integrity & durability. The initial step involves identifying and addressing redundancy in a table (or relation) caused by insert, update, and delete anomalies. Subsequently, the table is transformed into a normalized form by adhering to the rules of normalization (1 NF, 2 NF, 3 NF, BCNF, etc), which typically include decomposing/breaking it down into multiple tables to eliminate redundancy, and keep on breaking it until it addresses all insert/update/delete anomalies in all decomposed tables.
Normalization offers many advantages for database administration and performance, such as reducing the amount of data storage and memory usage, preventing data anomalies, simplifying the maintenance and modification of data, and enhancing the security and accessibility of data. By normalizing data, queries and transactions can become faster and more efficient. Moreover, insertion, deletion, and update anomalies can be avoided since data is not duplicated or inconsistent across tables. Additionally, changes only need to be made in one place and can be propagated to related tables automatically. Finally, different tables can have different levels of permissions and access rights for users and applications.
The main benefit of Normalization is that the database now is free from Insert, Update and Delete anomalies. There are no chances of data loss, unwanted overwrites, duplicates, or any kind of redundant data.
Normalization is important in SQL (Structured Query Language) and relational databases for several reasons: 1. Minimizing Data Redundancy: Normalization helps in reducing data redundancy by organizing data into separate tables and eliminating duplicate information. 2. Preventing Update Anomalies: Update anomalies occur when changes to data are not consistently applied across all instances of that data. Normalization helps to minimize these anomalies. 3. Improving Data Integrity: By reducing redundancy and ensuring that data is stored in a consistent manner, normalization helps maintain data integrity.
Normalization is not a perfect solution for every database scenario, and it comes with some drawbacks that need to be taken into account. For one, normalization can increase the complexity and number of tables, making the database design and documentation more challenging and time-consuming. Additionally, it can reduce the query performance and response time, as more joins and indexes are necessary to retrieve data from multiple tables. Furthermore, it can limit the flexibility and functionality of the database, as some queries and operations may not be possible or optimal with a normalized structure.
The main drawback of normalization is that as you keep on breaking the tables into multiple tables, querying back the data gets more costly, as it involves JOINing multiple tables which takes time thus reducing performance of the query and data retrieval.
Normalization is usually broken down into stages, known as normal forms. Each normal form has a set of criteria that must be satisfied for a table to be considered normalized. The most popular normal forms are First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF). 1NF requires that a table has no repeating groups or arrays of data, and each column holds a single value and has a unique name. 2NF states that every non-key column must depend on the whole primary key, not just a part of it. 3NF states that every non-key column must depend only on the primary key, not on any other non-key column. Higher normal forms, such as Boyce-Codd Normal Form (BCNF), Fourth Normal Form (4NF), and Fifth Normal Form (5NF) are less commonly used and not covered in this article.
There are several normal forms (NF) that define different levels of normalization, each building on the rules of the previous one. The most common normal forms include: First Normal Form (1NF): Second Normal Form (2NF): Third Normal Form (3NF): Boyce-Codd Normal Form (BCNF): Fourth Normal Form (4NF): Fifth Normal Form (5NF):
As per industry standard if you attain up to first 3 normal forms then your database can be considered as Integrated and safe for general purpose querying and data update. 1st NF talks about atomic values and non-repeating groups. 2nd NF enforces that a non-Key attribute should belong to entire Key attribute. 3rd NF makes sure that there should be no transitive dependency between a non-Key and a Key attribute.
Normalizing a table in SQL requires following the rules of each normal form and applying them to the table design. This may involve creating new tables, adding or removing columns, and defining primary keys and foreign keys. To illustrate, let's consider a Customers table with various columns and data. This table is not normalized, as it has repeating groups of data, partial dependencies, and transitive dependencies. To normalize this table, you can apply the following steps: (1) Achieve 1NF by splitting the table into two tables: one for customer information and one for order information; (2) Achieve 2NF by splitting the Orders table into two tables: one for order details and one for product details; (3) Achieve 3NF by splitting the Customers table into two tables: one for customer name and one for customer address. After these steps are applied, you will have a normalized database with no redundancy, partial dependencies, or transitive dependencies. You can then use SQL queries to join the tables and retrieve the data you need.
Step 1: First Normal Form (1NF), ensure that each cell in the table holds atomic (indivisible) values, there are no repeating groups, and each row has a unique identifier/ID. Step 2: Second Normal Form (2NF), ensure that there are no partial dependencies. In other words, every non-prime attribute is fully functionally dependent on the primary key. Step 3: Third Normal Form (3NF), ensure that there are no transitive dependencies. In other words, every non-prime attribute is non-transitively dependent on the primary key.
To Normalize or not to, depends on the application & database type you are dealing with. If you have an OLPT application then it makes sense to normalize a big tables into smaller tables as per NF forms. But if you are dealing with an OLAP or a reporting application then it makes sense to denormalize the tables, which means to combine multiple tables into one big table to avoid JOINs cost while querying the data for reporting purpose.
We also need to consider Normalisation from the OLTP vs OLAP perspective as the optimal level of normalisation depends on the specific database's purpose and usage patterns. - Normalizing OLTP databases is logical as it prioritizes effectively managing substantial transaction volumes in real-time, ensuring data integrity, consistency, and streamlined transaction processing. Typical Normal Forms:?3NF or even higher for mission-critical data are typical. - For OLAP databases, denormalization is advisable as it emphasizes aiding intricate analytical queries and data mining for decision-making, frequently improving query speed, streamlining analysis, and accommodating multi-dimensional data models. Typical Normal Forms:?1NF or 2NF