Decomposition in DBMS

Why Do We Need Decomposition?

In the previous topic, we saw problems like insertion, update, and deletion anomalies. These issues occur because all data is stored in a single table.

Decomposition solves these problems by splitting the table into smaller, meaningful tables.

📊 Example (Before Decomposition)

Here, course details are repeated multiple times, which can lead to anomalies.

✨ After Decomposition

• Student Table: StudentID, Name
• Course Table: Course, Fee
• Enrollment Table: StudentID, Course

Now data is separated properly and redundancy is removed.

Types of Decomposition

• Lossless Decomposition
• Dependency Preserving Decomposition

🔗 Lossless Decomposition

Lossless decomposition ensures that no data is lost when we join the tables again.

💡 Simple Meaning

If we break a table and later join it back, we should get the exact original table.

"Break karo, but data kabhi lose nahi hona chahiye."

⚙️ Dependency Preserving

Dependency preserving means all functional dependencies should still be maintained after decomposition.

💡 Simple Meaning

We should not lose any relationship between attributes after splitting tables.

"Relations safe rehne chahiye, warna logic toot jayega."

Final Summary

• Decomposition removes redundancy
• Helps eliminate anomalies
• Creates structured database design
• Two main types: Lossless & Dependency Preserving

Decomposition is a key step in normalization and database design.