Normalization is a special database design technique that lessens data redundancy and improves database design. In database normalization, the repetitive data is reduced. This means that this technique divides bigger tables into smaller tables. Moreover, the normalization techniques link tables by relationships. So the main objective of the normalization in SQL is to reduce redundant/boring information and confirm data is stored rationally.
However, this SQL normalization helps us to collect related information by traversing across relational tables. Any prenominal data or indirectly correlated figures are put in different tables. Then, these tables are linked with a rational connection between parent and child tables. For this reason, database normalization is considered a well-known and more popular technique. In short, if you want to know what is normalization, database normalization basics, or its several normal forms and functioning, then read this write-up till the end. We will describe the theory, model, basics, tables, and related database tables schema as well.
Let’s start our discussion!
What Is Normalization?
Normalization is the division of the relational model that offers design perceptions. It is the procedure of determining how much redundancy occurs in a given table. The intention of normalization is to provide mechanisms for converting schemas to reduce redundancy, improvement in database design, and security.
Also, this technique draws greatly on the theory of addressing practical dependencies. At the moment the normalization theory explains six normalization forms. Moreover, every normal form contains a set of dependency characteristics that a schema must fulfill and each normalization form gives assurance about the resolution of provided data anomalies. So it means that advanced normalization forms have less redundancy, and as a result, more meaningful data and a better definition of a given database schema.
Rational Database Normalization
When developing the scheme of an interactive database, one of the best characteristics to be taken into account is to guarantee that the repetition is diminished. However, this is in the case of rational database normalization. Let’s discuss the main purpose of this action/minimization.
- Decreasing the storage amount required to store the information.
- Circumventing avoidable data clashes that can creep in because of numerous copies of the similar information getting stored.
Database Normalization Theory
The designer of the relational model, Edgar Codd in 1970 suggested the theory of data normalization with the overview of the first normal form. Furthermore, he continued to explain the theory with other 2nd and 3rd normal forms. Then he joined Raymond F. Boyce to give another Boyce-Codd Normal Form theory.
Normalization in DBMS
Undeniably, database normalization is a method that assists in scheming the plan of the database in an optimum way to confirm the storage capacity. The basic idea of normalization is to split the tables into small tables and store indicators/references to information rather than repeating it. Therefore, this normalization in the DBMS model or technique is perfect to overcome or reduce the data repetition problem.
What are the Types of Database Normalization Forms?
The database normalization forms vary and classify into different categories depending upon their specific characteristics. Now we will enlist all normal forms here such as:
- 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)
- Sixth normal form (6NF)
Obviously, the Concept of Data Normalization in SQL server is still a great source of debate. For example, developers believe that it’s totally non-realistic to even think of implementing anything after 4NF. Though, in most software applications, normalization attains its best in 3NF Normal Form. The advancement of SQL Normalization theories can be explained in different ways.
Database Normalization with Table Examples
Example 1:
For a better understanding of what we just said, here is a simple DBMS Normalization example: To comprehend normalization in the DBMS or database with sample tables, let’s consider that we are assumed to store the information of courses and trainers of a given university. Here is what a sample table database may look like:
Table
| Course Code | Course Venue | Trainer Name | Trainer’s contact number |
| CS200 | Lecture theater 1 | Prof. John | +75 017654328 |
| CS701 | Lecture room 2 | Prof. Sam | +75 087654125 |
| CS702 | CAS lecture room | Prof. John | +75 017654328 |
Here, the records fundamentally store the subject course code, course venue, trainer name, and trainer’s contact number.
Firstly, this design looks very good and pretty. Nonetheless, problems start to happen once we need to alter data. For example, assume, if Prof. John changed his contact number. In such a condition, we will have to make revisions in two places in the above table. What if somebody just corrected the phone number against CS200, but forgot to change it for CS702? This will lead to incorrect figures in the database.
So this issue can be resolved easily by creating or dividing this whole information into another simple table.
Table # 1 (Trainer)
- Trainer Name
- Trainer ID
- Trainer mobile number
Table # 2 (Course info)
- Course code
- Instructor ID
- Course venue
At this time, our information will appear in a different way such as:
Table 1 (Trainer):
| Trainer’s ID | Trainer’s name | Trainer’s number |
| 10 | Prof. John | +75 017654328 |
| 11 | Prof. Sam | +75 017654328 |
Table 2 (Course info):
| Course code | Course venue | Trainer ID |
| CS200 | Lecture theater 1 | 10 |
| CS701 | Lecture room 2 | 11 |
| CS702 | CAS lecture room | 10 |
Principally, we store the trainers independently and in the course info table, we do not add the complete information of the instructor. We relatively store the ID of the teacher. At this moment, if somebody wants to know the contact details of the trainer, he can just look up the trainer table by writing a standard JOIN query or by a simple WHERE clause . Likewise, if we were to alter the phone number of Prof. John, it can be changed in exactly one column. This circumvents the stale or incorrect data problem.
Additionally, if you notice, now the contact number now requires not to be kept two times. We have stored it in only one place. This also protects and ensure the best use of data storage. This might not be clear in the above simple table example. But, think about the situation when there are thousands of courses and teachers and for each trainer, we have to save not just the contact details but also the email addresses, their home or office address, availability, qualification, expertise, etc.
In such a circumstance, duplicating so many figures will simply increase the storage requirement needlessly. And by the way, we have just implemented 1NF (first normal form) successfully. Easy right?
Example 2
The normalization process can be simply understood with the aid of a case study. Suppose, a video library keeps a record of movies being rented out to customers. We can place all the information or data in one table without the normalization process. Such as:
Table 1
| Name | Complete Address | Rented Movies | Salutation |
| Sam Johns | Street 2, 4th floor | The Hunger Games, Fantasy Island | Ms. |
| Smith James | Street 5, 2nd floor | The Wizard of Oz, 12 Angry Men | Mr. |
| John Doe | Street 3 | The Lord of the Rings | Mr. |
Here you can observe that the rented movies column has numerous values. Let’s discuss the all of the database normalization example step by step starting with first normal form.
1: First Normal Form (1NF)
There are few specific rules for the 1NF such as:
- Every table box should comprise a single value.
- Every record should be completely unique.
If we pay close attention to our primary table, we clearly notice that it demands 2 sub-tables. One related to customer’s info (we name it as Customer Table). The second one should contain movies title and references of the customers by using foreign keys (we name it as Rented Movies).
Customer:
| ID | Names | Complete Address | Salutation |
| 1 | Sam Johns | Street 2, 4th floor | Ms. |
| 2 | Smith James | Street 5, 2nd floor | Mr. |
| 3 | John Doe | Street 3 | Mr. |
Rented Movies:
| Customer_ID | Rented_Movies |
| 1 | The Hunger Games, Fantasy Island |
| 2 | The Wizard of Oz, 12 Angry Men |
| 3 | The Lord of the Rings |
Here, one important aspect to notice is the introduction of Customer_ID as foreign key inside Rented Movies table. Before these keys start to sound complicated to you, lets quickly discuss the common types of keys that we use in Database Normalization.
What is meant by a Key?
A Key is a value that helps to recognize a record in a table distinctively. It can be a single column as well as a mixture of various columns. And those columns that are not unique can be considered as non-key columns or values.
Primary Key:
A primary key is defined as the single-column value that helps to recognize a database record uniquely.
Primary key possesses the following unique features such as:
- A primary key should not be Null.
- Its value should be unique
- Its values should hardly be altered
- It must be given a value while new data is introduced. Preferably, auto-increment.
- It must be an integer.
Composite Key:
- A composite key is a primary key comprises of several columns used to detect a record distinctively. It is often a combination of a primary key with a repetitive entity to introduce uniqueness into the equation. It’s barely used nowadays.
Foreign Key:
- Key has a different name from its relevant primary key.
- It guarantees rows in one table have parallel rows
- On contrary to the Primary key, they do not require to be unique.
- It can be null while the primary key should not be null.
- They can be repetitive.
- These keys can be set to a strict relationship with parent table. Example, by implementing foreign key constraint to not let it delete until its parent primary key is being deleted.
- Gives you an easy access to ensure and maintain data integrity.
2: Second Normal Form (2NF)
We can explain the 2NF example with the help of the above-mentioned table. There are some specific rules to be followed while explaining the 2NF example.
- The database tables must be in first normal form
- There should be a single-column primary key
- Address the data that is only partially dependent on the primary key and then group all of that data in another table.
Main data table:
| Names | Complete Address | Rented Movies | Salutation |
| Sam Johns | Street 2, 4th floor | The Hunger Games, Fantasy Island | Ms. |
| Smith James | Street 5, 2nd floor | The Wizard of Oz, 12 Angry Men | Mr. |
| John Doe | Street 3 | The Lord of the Rings | Mr. |
Customer:
| ID | Names | Complete Address |
| 1 | Sam Johns | Street 2, 4th floor |
| 2 | Smith James | Street 5, 2nd floor |
| 3 | John Doe | Street 3 |
We create another table with the name Surnames while devising the main table in our 2nf example.
Surnames:
| Customer_ID | Surname |
| 1 | Ms. |
| 2 | Mr. |
| 3 | Mr. |
And, then comes the Rented Movies table.
Rented Movies:
| Customer_ID | Rented_Movies |
| 1 | The Hunger Games, Fantasy Island |
| 2 | The Wizard of Oz, 12 Angry Men |
| 3 | The Lord of the Rings |
We have separated our First Normal Form tables into three separate tables. Astable Customer contains the key info such as Names and Complete Address. Then, we figured out that Surnames are another set of extended Customer information that we can put inside a new table, hence naming it as Surnames. The third table contains information about the Rented Movies to the customers. Therefore, we have just split the data into more child tables just to give a better separation of concerns. This is one of the prime database normalization example steps by step.
3: Third Normal Form (3NF) and its Rules
In 3NF normalization, you should follow the two rules:
- Be in 2nd normal form (2NF)
- Has no transitive functional dependencies. In other words, the data should be subjected to 3NF if it is totally independent of the Primary key.
Transitive Functional Dependencies It occurs when altering a non-key column, which could cause any of the other non-key table columns to alter.
To explain 3Nf normalization form, let’s add two columns named as Hobby and Skills.
| ID | Full Name | Address | Rented Movies | Hobby | Skills | Surname |
| 1 | Same johns | Street 2, 4th floor | The Hunger Games, Fantasy Island | Internet | Coding | Mr. |
| 2 | Smith James | Street 5, 2nd floor | The Wizard of Oz, 12 Angry Men | Fishing | Accounting | Mr. |
| 3 | John Doe | Street 3 | The Lord of the Rings | Reading | Doctor | Mr. |
Surnames:
| Salutation (ID) | Salutation |
| 1 | Mr. |
| 2 | Mr. |
| 3 | Mr. |
Customer_Metadata
| Hobby | Skills |
| Internet | Coding |
| Fishing | Accounting |
| Reading | Doctor |
Rented Movies:
| Customer_ID | Rented_Movies |
| 1 | The Hunger Games, Fantasy Island |
| 2 | The Wizard of Oz, 12 Angry Men |
| 3 | The Lord of the Rings |
While comparing it with 2NF, the only change that we did here is to make a new table Customer_Metadata and storing the information of the customer that we believed is independent of the customer’s primary key. Of course, Customer_Metadata will have the unique ID for each of its columns. This is the complete 3NF database normalization example step by step.
3: Boyce-Codd Normal Form (BCNF)
When a record is in 3NF, still there might be irregularities caused if it has more than 1 key (candidate). It is also considered as 3.5NF.
4: Fourth Normal Form (4NF)
If no record table case comprises two or more, multivalued data stating its dependency other than a candidate key, then it is referred to as the fourth Normal Form (4NF).
5: Fifth Normal Form (5NF)
A value is called the fifth Normal Form only if it is in 4th normal form. And it cannot be disintegrated into any number of smaller Sub tables without loss of information.
6: Sixth Normal Form (6NF)
6NF is not a standard because it is being deliberated by database specialists for some time. Expectantly, we would have a strong and standardized description for 6NF soon.
What are the Advantages of DBMS Normalization?
You can observe the several advantages of DBMS normalization during the data storing process. DBMS Normalization process provides the following important benefits such as:
- It increases data reliability as it evades the duplicity of information by storing the records in only one place.
- The normalization process assists in assemblage similar or related information under a similar schema, thus resulting in the improved grouping of records.
- Normalization causes searching quicker as indexes can be produced faster. Therefore, the normalized database tables are used for Online Transaction Processing (OLTP).
What are Detriments/Drawbacks of Database Normalization?
Consequently, DBMS Normalization has also some disadvantages. Now let discuss a few of them as below:
- An increase in tables means more DB operations.
- More operations mean an increase in I/O operations.
- Makes data processing expensive.
- Applying JOIN on top of normalized tables proves both times consuming and compute-intensive operation.
- Don’t dive too much and unnecessarily into it. Up to 2NF or a max 3NF is all good.
- De-normalize the tables whenever necessary, to improve I/O operations.
VERDICT
In a nutshell, database scheming is thoughtful to the successful application of a database management system that fulfills the information requirements of an enterprise organization. This process helps to produce database systems that are economical and have improved safety models.
Henceforth, practical dependencies are a very essential module of the normalized data procedure Maximum database systems are normalized up to the 3rd normal forms (3NF normalization). Moreover, a primary key matchlessly recognizes cannot be null. And, a foreign key supports the connect table and places a primary key. Each Software developer devises and implements DB Normalization as per his own way and scheme of thoughts. So it’s perfectly normal to see two different normalization techniques being applied by two different developers. Database Normalization is good but its usage shouldn’t be abused. Only apply where you think it’s desperately needed.
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