Normalization

1. DBMS Normalization
• Normalization is a process in Database
Management Systems (DBMS) that
organizes the attributes and relations in a
database to reduce redundancy and
improve data integrity.
• Normalization aims to eliminate unwanted
characteristics like insertion, update, and
deletion anomalies.

2. What is Normalization?
• Normalization in DBMS is the process of
organizing the fields and tables of a
relational database to minimize
redundancy and dependency.
• The goal is to ensure that the data is
stored in a way that reduces duplication
and maintains data integrity.

3. Why Normalize Data?
• 1. Reduces redundancy and avoids data
duplication.
• 2. Minimizes the chances of anomalies like
update, insertion, and deletion anomalies.
• 3. Ensures data consistency and integrity.
• 4. Enhances the efficiency of queries.

4. Normal Forms (1NF, 2NF, 3NF, BCNF)
• 1. 1NF (First Normal Form): Ensures that
all columns contain atomic values and no
repeating groups.
• 2. 2NF (Second Normal Form): Ensures
that all non-key attributes are fully
functionally dependent on the primary key.
• 3. 3NF (Third Normal Form): Ensures that
all non-key attributes are not transitively
dependent on the primary key.
• 4. BCNF (Boyce-Codd Normal Form): A
stronger version of 3NF where every

5. 1st Normal Form (1NF)
• A relation is in 1NF if:
• 1. All columns contain atomic (indivisible)
values.
• 2. Each column contains only one value
per row.
• 3. There is a unique identifier (Primary
Key) for each record in the table.

6. 2nd Normal Form (2NF)
• A relation is in 2NF if:
• 1. It is in 1NF.
• 2. Every non-prime attribute (attribute not
part of the candidate key) is fully
functionally dependent on the entire
candidate key (no partial dependencies).

7. 3rd Normal Form (3NF)
• A relation is in 3NF if:
• 1. It is in 2NF.
• 2. There are no transitive dependencies,
i.e., non-prime attributes do not depend on
other non-prime attributes.

8. Boyce-Codd Normal Form (BCNF)
• A relation is in BCNF if:
• 1. It is in 3NF.
• 2. For every functional dependency (X ->
Y), X is a superkey.

9. Normalization Process
• The normalization process involves:
• 1. Identifying candidate keys for the
relation.
• 2. Breaking down relations based on the
rules for 1NF, 2NF, and 3NF.
• 3. Removing partial and transitive
dependencies.

10. Examples of Normalization
• Example 1: If a table has repeating groups
of data, it can be split into multiple tables
to adhere to 1NF.
• Example 2: If a table has partial
dependencies, split it further into tables to
achieve 2NF.

11. Advantages of Normalization
• 1. Reduces redundancy and ensures data
consistency.
• 2. Simplifies database design.
• 3. Helps in easier data management and
maintenance.
• 4. Prevents data anomalies during
updates.

12. Disadvantages of Normalization
• 1. Complex queries due to multiple tables.
• 2. Possible performance overhead in
joining tables.
• 3. Increased complexity in database
design.
• 4. More storage required in some cases.

13. When to Use Normalization
• Normalization is beneficial when:
• 1. The focus is on reducing redundancy.
• 2. Data integrity and consistency are
priorities.
• 3. The database is expected to have
complex relationships.
• Denormalization is used when
performance is a priority.

14. Denormalization and its Uses
• Denormalization is the process of
combining tables to reduce the number of
joins in complex queries.
• Denormalization is typically used to
optimize query performance at the cost of
increased redundancy.

15. DBMS Normalization
• Normalization is a systematic approach to ensure that a database is free from redundancy and
inconsistent data. By applying specific rules, normalization transforms a database into a structure
that improves both the integrity and efficiency of data.
• The primary aim of normalization is to avoid various anomalies and make it easier to manage
large databases with many interrelated tables.

16. What is Normalization?
• Normalization in DBMS refers to the process of structuring a relational database in such a way
that:
• 1. Data redundancy is minimized.
• 2. Data integrity is ensured by eliminating anomalies.
• 3. Tables and relations are divided logically to ensure efficient storage.
• This is done by breaking down larger tables into smaller, more manageable ones and
establishing clear relationships between them.

17. Why Normalize Data?
• 1. **Reduce Redundancy:** Minimizes duplicate data entries, reducing storage requirements.
• 2. **Eliminate Anomalies:** Helps avoid insertion, update, and deletion anomalies by organizing
data efficiently.
• 3. **Data Integrity:** Ensures accuracy and consistency of the data by maintaining clear
dependencies between attributes.
• 4. **Efficiency in Querying:** Optimizes queries, reducing the amount of data processed and
making it easier to modify data without complications.

18. Normal Forms (1NF, 2NF, 3NF, BCNF)
• 1. **1NF (First Normal Form):** Ensures that the table contains only atomic values. This removes
repeating groups or arrays.
• 2. **2NF (Second Normal Form):** Achieved when a relation is in 1NF and all non-key attributes
are fully functionally dependent on the primary key.
• 3. **3NF (Third Normal Form):** A relation is in 3NF if it is in 2NF and all non-key attributes are
independent of each other.
• 4. **BCNF (Boyce-Codd Normal Form):** A stricter version of 3NF where every determinant is a
candidate key.

19. 1st Normal Form (1NF)
• A relation is in 1NF if:
• 1. It contains only atomic (indivisible) values in each column.
• 2. Each row has a unique identifier (Primary Key) that ensures there are no duplicate records.
• **Example:**
• In a table containing students and subjects, where each row has multiple subjects listed, breaking
that into separate rows with atomic values for each student-subject pair ensures 1NF.

20. 2nd Normal Form (2NF)
• A relation is in 2NF if:
• 1. It is in 1NF.
• 2. Every non-prime attribute is fully functionally dependent on the primary key (i.e., no partial
dependencies).
• **Example:**
• If a table contains student data with a primary key of 'student_id' but the 'student_name' depends
only on 'student_id', then splitting the table ensures that each non-key attribute is fully dependent
on the key.

21. 3rd Normal Form (3NF)
• A relation is in 3NF if:
• 1. It is in 2NF.
• 2. It does not have any transitive dependencies (i.e., non-key attributes do not depend on other
non-key attributes).
• **Example:**
• If a table contains 'student_id', 'student_name', and 'advisor_name', where 'advisor_name'
depends on 'student_name' rather than the 'student_id', 3NF would involve removing this
transitive dependency.

22. Boyce-Codd Normal Form (BCNF)
• BCNF is a stricter form of 3NF where:
• 1. A table is in BCNF if it is in 3NF.
• 2. For every functional dependency (X -> Y), X is a superkey.
• **Example:**
• In a table with 'employee_id' and 'project_id' as keys, if a project determines the employee
working on it (not the other way around), BCNF ensures that 'project_id' is not a determinant
unless it's a candidate key.

23. Normalization Process
• The normalization process follows these general steps:
• 1. Identify functional dependencies between attributes.
• 2. Eliminate partial and transitive dependencies.
• 3. Decompose the tables based on these dependencies.
• 4. Apply the rules for 1NF, 2NF, 3NF, and BCNF iteratively until the relation is in the highest
applicable normal form.

24. Examples of Normalization
• 1. **1NF Example:** A table that stores customer orders with a column that lists multiple products
can be broken down into separate rows for each product.
• 2. **2NF Example:** In a student-course table, splitting the data into a 'students' table and a
'courses' table helps eliminate partial dependencies.
• 3. **3NF Example:** A 'student-course' table might be further split into separate tables for
'student', 'course', and 'advisor' to eliminate transitive dependencies.

25. Advantages of Normalization
• 1. **Eliminates Data Redundancy:** Normalization ensures that data is stored once, reducing
space and inconsistencies.
• 2. **Prevents Anomalies:** By minimizing redundancy, insertion, update, and deletion anomalies
are prevented.
• 3. **Improved Data Integrity:** The structure ensures that data changes do not result in
inconsistencies.
• 4. **Simplifies Data Maintenance:** With fewer dependencies, managing the data is easier and
more consistent.

26. Disadvantages of Normalization
• 1. **Complex Queries:** Because data is split into many smaller tables, queries may require
multiple joins, which can be inefficient.
• 2. **Increased Complexity:** Managing and designing normalized databases can be more
complex than denormalized ones.
• 3. **Performance Overhead:** For very large databases, normalization can result in slower query
performance due to the need to join many tables.

27. When to Use Normalization
• Normalization should be applied when:
• 1. The focus is on minimizing redundancy and ensuring data consistency.
• 2. The database is expected to evolve over time with frequent updates.
• 3. Data integrity and accuracy are crucial.
• **When Not to Use Normalization:**
• When performance is more important than data redundancy, denormalization may be used to
simplify the structure and improve query speed.

28. Denormalization and its Uses
• Denormalization is the process of introducing redundancy into a database by merging tables. It is
used when:
• 1. **Performance is a priority:** Fewer joins mean faster query performance.
• 2. **Simplifying queries:** Denormalized databases may be easier to query for certain
applications.
• 3. **Data retrieval needs:** When frequent read-heavy operations are needed over write
operations.