What is Normalization?
Normalization is the process of organizing data to minimize redundancy and dependency. It involves splitting data into multiple related tables and defining relationships using foreign keys.
Commonly used in: OLTP (Online Transaction Processing) systems.
Benefits
- Reduces data redundancy.
- Maintains data integrity.
- Saves storage space.
Drawbacks
- Increases the number of tables.
- Requires complex joins for queries.
- Slower performance in analytical workloads.
Example
A "Customer" table references a separate "City" table using a foreign key instead of storing city names in every record.
What is Denormalization?
Denormalization is the process of combining normalized tables into fewer tables by introducing redundancy. It aims to improve read performance, especially in analytical scenarios.
Commonly used in: OLAP (Online Analytical Processing) systems / Data Warehouses.
Benefits
- Faster query performance.
- Simpler data structures.
- Easier to use in BI tools (e.g., Tableau, Power BI).
Drawbacks
- Data redundancy increases.
- More storage required.
- Updates and maintenance become more complex.
Example
A single "Customer" table includes all fields like customer name, city name, region, and country directly, without referencing separate dimension tables.
Normalization vs. Denormalization – Comparison
| FFeature |
Normalization |
Denormalization |
| Data Redundancy |
Minimal |
Can be high |
| Storage Efficiency |
High |
Lower |
| Query Performance |
Slower (more joins) |
Faster (fewer joins) |
| Ease of Use |
Complex for analytics |
Simple for reporting |
| Maintenance |
Easier updates |
Harder to maintain |
| Use Case |
OLTP systems |
OLAP / BI systems |
When to Use Which?
-
Normalize when:
- System is write-heavy (e.g., banking transactions)
- Data accuracy and integrity are critical.
- Storage space is limited
-
Denormalize when:
- System is read-heavy (e.g., dashboards)
- Query performance is a priority
- You use BI tools that prefer flatter structures
📌 Real-World BI Tip
In a real-world BI environment, normalized tables are often used in staging layers, and denormalized tables are used in reporting layers or data marts. This separation helps maintain the best of both worlds: clean, reliable source data and fast, accessible reporting data.
Conclusion
Both normalization and denormalization have their place in a well-architected data warehouse. Rather than choosing one over the other entirely, the best practice is to use normalization for data quality and consistency, and denormalization for speed and usability in the reporting layer.
Striking the right balance is the key to building a powerful, scalable, and efficient BI system.