Denormalization is the process of intentionally introducing redundancy into a database by combining normalized tables — often for performance or simplicity reasons.
It’s the reverse of normalization, done not because of bad design, but to optimize read-heavy operations.
🎯 Why Use Denormalization?
- Improve Read Performance
- Reduces the number of joins needed in complex queries.
- Speeds up data retrieval in analytics and reporting.
- Simplify Queries
- Fewer tables to query means easier SQL for developers.
- Caching or Materialized Views
- Pre-aggregated or summary data can be denormalized to improve performance.
❌ Downsides of Denormalization
- Data redundancy → more storage
- Update anomalies → harder to keep redundant data consistent
- Complex inserts/updates → more logic needed to maintain correctness
✅ Example: From Normalized to Denormalized
🔹 Normalized Structure
Customers
| CustomerID | Name |
|---|---|
| 1 | Alice |
| 2 | Bob |
Orders
| OrderID | CustomerID | Product |
|---|---|---|
| 101 | 1 | Laptop |
| 102 | 2 | Phone |
To get a customer’s name and product, you’d need a JOIN:
SELECT Orders.OrderID, Customers.Name, Orders.Product
FROM Orders
JOIN Customers ON Orders.CustomerID = Customers.CustomerID;
🔸 Denormalized Structure
Orders
| OrderID | CustomerName | Product |
|---|---|---|
| 101 | Alice | Laptop |
| 102 | Bob | Phone |
Now:
- ✅ No join needed
- ❌ But
CustomerNameis duplicated, and must be kept consistent across records
🧠 When to Use Denormalization?
Use it carefully when:
- Your database is read-heavy
- Joins are slowing down performance
- Data doesn’t change often (e.g., reporting systems)
- You’re working with NoSQL or data warehouses