This content originally appeared on DEV Community and was authored by srivishal
Guide to Database Normalization (1NF → 3NF)
Normalization is the art of organizing data efficiently — avoiding redundancy and ensuring consistency.
In this post, we’ll break down how to transform an unorganized student-course dataset into its 1NF, 2NF, and 3NF forms step by step with SQL code and outputs.
Identify Anomalies
Insertion anomaly:
Can’t insert a new course unless a student enrolls.
Update anomaly:
If Dr. Kumar’s phone number changes, we must update multiple rows.
Deletion anomaly:
If student S02 drops the course, we lose info about the DBMS course entirely.
The Raw Table — 1NF (First Normal Form)
In the beginning, everything lives in one big table — students, courses, instructors, and even phone numbers.
Creation of table
Insertion of data
Output
2NF (Second Normal Form)
To fix redundancy, we break our data into four tables:
Create separate tables for Students, Courses, Instructors, Studentcourses.
Insertion of values
Output
Convert to 3NF (Third Normal Form)
Remove transitive dependency (non-key attributes shouldn’t depend on other non-keys).
In our case, the InstructorPhone depends on Instructor, not directly on Course.
So we already separated Instructors, which makes this 3NF compliant.
Final tables in 3NF:
Students(StudentID, StudentName)
Instructors(Instructor, InstructorPhone)
Courses(CourseID, CourseName, Instructor)
StudentCourses(StudentID, CourseID)
JOIN: Combine Everything
Why Normalization Matters
Prevents data redundancy
Improves data consistency
Makes updates easier and safer
Ensures relational integrity
Key Takeaway
Database normalization is not just a theory — it’s what keeps your database organized, clean, and scalable.
Every efficient application you use today, from Instagram to banking apps, relies on normalized relational databases underneath.
This content originally appeared on DEV Community and was authored by srivishal