Normal Forms in DBMS: Reducing Data Redundancy and Avoiding Anomalies

What causes the databases you use most frequently to become filled with repeated entry information? Does changing one piece of information often result in a ‘snowball effect,’ where correcting one problem throws another one into the mix?

These are typical scenarios in the database management domain, which may result from insufficient normalisation.

Data normalisation is the key process that helps maintain a clean, efficient, and easily manageable database.

It makes a significant contribution to the normal functioning of a database through its purpose of putting all data in its rightful place and allowing for future expansion or growth without adverse outcomes. In simple terms, normal forms in DBMS are about minimising redundancy in data and avoiding problems such as insertion, deletion, and update anomalies.

But what do normal forms in DBMS mean? Now, it’s time to get deeper into details and understand why these concepts are crucial.

Importance and Benefits of Normalisation in DBMS

Now, the question that arises here is: why does normalisation matter?

Suppose you are the manager of student records in a school. Each time a student moves to a new address, you update a record for each course in which the student is enrolled. If you miss an update, you end up with confusing and often contradicting details.

This is where normal forms in DBMS come in.

By structuring the data properly, you only need to store the student’s address once, and any changes are automatically reflected everywhere.

Here’s what normalisation brings to the table:

• Ensures that data are consistent across the database.
• Minimises duplicate data, saving storage space and making the database more efficient.
• Maintain accurate and reliable data by eliminating anomalies.

But normalisation isn’t just about reducing headaches; it’s about creating a foundation that supports future growth.

As your database grows, a well-normalised structure will handle the increasing complexity with ease.

Defining Data Redundancy and Its Impact on Databases

Data redundancy occurs when the same piece of data is stored in multiple places within a database. As the database grows, redundancy can lead to serious problems.

Imagine storing the same customer’s contact details in five different tables. If the customer changes their phone number, you must update all five records.

Miss one, and you’ve got conflicting data, which can cause confusion and errors.

This is where normal forms in DBMS shine.

By organising the data to eliminate redundancy, we can ensure that each piece of data is stored only once. This makes updates simpler and prevents the inconsistencies that redundancy can cause.

Understanding Database Anomalies: Insertion, Deletion, and Update Anomalies

Anomalies are errors or inconsistencies that occur when data is added, deleted, or updated in a database.

They are the unwanted side effects of poor database design.

Insertion Anomalies:

These happen when you can’t insert data into a table because other data is missing.

For example, if you’re adding a new student to a database but can’t do so because their course information isn’t available yet, that’s an insertion anomaly.

Deletion Anomalies:

These occur when deleting data unintentionally removes other valuable data.

For instance, if deleting a course record also removes the details of the students enrolled in that course, you’ve encountered a deletion anomaly.

Update Anomalies:

These arise when updating a single piece of data, which requires multiple updates across the database.

If you change a customer’s address in one table but forget to do so in another, you’ve got conflicting information—an update anomaly.

Detailed Exploration of Normal Forms in DBMS

Most of the problems are very well prevented by knowing and applying the different normal forms in DBMS. Now, let us discuss normal forms in DBMS and understand how these actually keep our databases clean and efficient.

First Normal Form (1NF): Ensuring Atomicity of Data

First Normal Form (1NF) is all about making sure every piece of data in your table is atomic.

Atomic means that each cell in a table contains only one value. No lists, no sets, just one value.

Imagine a table storing student contact information. If a single cell holds multiple phone numbers, that’s a problem.

Input Table (Before 1NF):

Code to Apply 1NF:

Output Table (After 1NF):

Second Normal Form (2NF): Eliminating Partial Dependencies

Moving to the Second Normal Form (2NF), we focus on getting rid of partial dependencies. This means every non-key attribute must be fully dependent on the primary key.

Consider a table where each student’s name depends only on their StudentID, but course details are also mixed in:

Input Table (Before 2NF):

Code to Apply 2NF:

Output Tables (After 2NF):

• Students Table:

• Courses Table:

• StudentCourses Table:

Third Normal Form (3NF): Removing Transitive Dependencies

The Third Normal Form (3NF) takes it a step further. Here, we remove transitive dependencies, where non-key columns depend on other non-key columns.

Let’s say we have a table where each student’s city depends on their postal code but not directly on the StudentID:

Input Table (Before 3NF):

Code to Apply 3NF:

Output Tables (After 3NF):

• StudentDetails Table:

• PostalCodes Table:

Boyce-Codd Normal Form (BCNF): A Stricter Version of 3NF

Boyce-Codd Normal Form (BCNF) is like 3NF but stricter. Every determinant in the table must be a superkey.

This implies that X should be a superkey for each functional relationship X → Y.

Consider a scenario where an employee’s project leader is determined by the ProjectID, but ProjectID isn’t a primary key:

Input Table (Before BCNF):

Code to Apply BCNF:

Output Tables (After BCNF):

• Projects Table:

• EmployeeProjects Table:

Fourth Normal Form (4NF): Handling Multi-Valued Dependencies

Fourth Normal Form (4NF) deals with multivalued dependencies. These occur when one column in a table depends on multiple values in another column.

For example, a table that lists students and their multiple hobbies:

Input Table (Before 4NF):

Code to Apply 4NF:

Output Tables (After 4NF):

• Students Table:

• Hobbies Table:

• Languages Table:

Fifth Normal Form (5NF): Ensuring Lossless Decomposition

Finally, the Fifth Normal Form (5NF) focuses on ensuring that data can be decomposed into smaller tables without losing any information.

This is crucial when dealing with complex relationships, such as product assemblies:

Input Table (Before 5NF):

Code to Apply 5NF:

Output Tables (After 5NF):

• Assemblies Table:

• Products Table:

• Suppliers Table:

Balancing Normalisation and Database Performance

Is your database running slower after applying all those normal forms?

Normalisation is great for reducing redundancy and improving data integrity, but it can sometimes slow down performance.

With all these normal forms, it’s easy to think more normalisation is always better. But the trick is to strike the right balance between normalisation and speed.

Too much normalisation can lead to complex queries requiring multiple joins. This can bog down your system, especially with large datasets.

So, what’s the solution?

Here’s how we can find the right balance:

• Start Simple: Normalise up to 3NF or BCNF for most applications.
• Monitor Performance: Keep an eye on query times and database performance.
• Denormalize When Needed: If performance issues arise, consider denormalizing selectively.

By balancing normal forms in DBMS with practical needs, we ensure our databases are both efficient and easy to work with.

Also read: Database Management System?

Best Practices for Implementing Normalisation in Real-World Databases

Know When to Stop:

While it’s tempting to normalise to the highest level, it’s not always necessary. Most of the time, it doesn’t go beyond BCNF or 4NF.

Test Early and Often:

After each step of normalisation, run queries to see how the database handles them. This helps catch performance issues early.

Document Your Design:

Keep a clear record of your normalisation process. It’s easier to troubleshoot and explain your design decisions if problems arise later.

Keep the Future in Mind:

Think about how your database will grow. What works for a small dataset might not scale well. Plan your normalisation with an eye on future expansion.

Conclusion

Normal forms in DBMS are important to deal with the issue of data redundancy, maintain data consistency, and avoid data anomalies.

Starting with the basic normal forms such as 1NF, 2NF, and 3NF, we bring a solid standard to the framework.

In complex data dependencies, it is necessary to move to BCNF and further. However, each level of normalisation brings an extra level of complexity and problem, which affects the performance.

This is the reason why those people working on databases should be able to know when they need to stop and when they should be denormalised with the aim of improving efficiency.

A well-designed database is both organised and, at the same time, optimised according to your application’s needs, granting both accuracy and speed in real-life situations.