Grokking Normalization vs Denormalization – How to Design Your Database Right

This blog covers the fundamentals of database schema design, focusing on normalization vs denormalization. You'll learn what these terms mean, the pros and cons of each approach, and how to choose the right balance for an efficient, scalable database.

Imagine you're designing a new app and setting up its database.

Should you organize data into many tables for perfect consistency, or duplicate some data in one place for speed?

This decision – to normalize or denormalize your schema – is like choosing between a tidy closet and keeping stuff in multiple spots for convenience.

One gives you order and accuracy, the other gives you speed and simplicity.

Understanding how SQL normalization and denormalization work (and when to use each) will make you a better database designer.

So let’s cover it together.

What is Database Normalization?

Normalization is a design approach that minimizes data redundancy (no duplicate data) and maintains consistency.

In practice, each piece of information is stored only once, in one table, and other tables refer to it via a relationship (a key) instead of having their own copy.

There are formal guidelines (called normal forms) that help achieve this (basically the rules of data normalization).

Most databases aim for Third Normal Form (3NF) as a good standard – indeed, many consider 3NF the answer to which normal form is best for typical applications.

For example, imagine an Orders table that stores customer details alongside each order.

If the same customer appears in multiple orders, their name and address repeat in every row.

To normalize, we create a Customers table for customer info (each customer stored once with a unique ID) and have the Orders table only store a reference to that ID.

Now if a customer’s address changes, we update it in one place (the Customers table) and all orders automatically reflect the new address.

No duplicated data means fewer inconsistencies and less storage waste.

The main benefit of normalization is clean, accurate data.

The trade-off is that to gather related info (say, order + customer details), the database must JOIN tables, which can make read queries a bit slower if many tables are involved.

What is Denormalization?

Denormalization is the deliberate step of relaxing the rules of normalization to gain speed.

It means combining tables or duplicating data so that queries can get everything they need from one place.

In our example, a denormalized design might store the customer name and address directly in each order record. This way, fetching an order requires no join at all – all the data is right there.

The big upside of denormalization is faster reads and simpler queries. This is especially useful in read-heavy scenarios (like analytics or feed pages) where performance is critical.

However, the downside is data redundancy.

With our denormalized orders, if a customer changes their address, that update needs to be made in every order row for that customer.

It also means using more storage and risking inconsistencies if not every copy is updated.

In essence, denormalization makes reading data fast but makes writing (updates/inserts) more cumbersome.

In practice, teams often use a mix of both approaches.

You might start with a normalized schema for accuracy and ease of management, then identify a few hotspots where joins are slowing things down and selectively denormalize those parts.

This hybrid approach gives you the best of both worlds: mostly clean data with a few performance boosts where necessary.