Here's how you can master database normalization techniques effectively.

- By eliminating duplicate data, normalization helps save storage space and ensures that updates are applied consistently. - . This means each column should hold a single value, and each record should be unique. - This refers to the relationship where one attribute uniquely determines another. - A well-normalized database is easier to manage and modify, as changes only need to be made in one place.

For understanding the concept of normalization, its very important to visualize the data. Normalization aims at removing data redundancy by splitting/dividing data into multiple tables and having a relation among the tables to JOIN the tables later for retrieving information. RDBMS databases are generally normalized. De-normalization is the opposite; it tends to maintain data redundancy by keeping as much information as possible in Same table thereby increasing the number of columns and amount of data stored in the table. This helps in retrieving results quicker and no Joins are needed. NoSQL databases use denormalization.

- Each column in a table must contain only atomic (indivisible) values. In other words, each cell in a column should hold a single value, not a set or list of values. - Each row in a table must be unique, meaning that no two rows should be identical. This uniqueness is typically ensured by a primary key. - All values in a column should be of the same data type. This ensures that the database maintains consistency and allows for proper querying and manipulation of the data. - A table should not have columns that contain repeating groups or arrays of data. Each piece of data should be stored in its own column.

1NF removes duplicated data from a cell of column & ensures only a single value is present there. E.g. Table Employee {emp_id, dept_id, phone_no} data row : [1001, 90, (67908732, 77889922)] Here phone_no column stores multiple values in a single cell which is against 1NF. To make it in 1NF, split the data into 2 separate tables, Employee & Phone, as below: e.g. Employee {emp_id, dept_id} Employee data row : [1001, 90] Phone {emp_id, phone_no} Phone data row : [1001, 67908732] data row : [1001, 77889922] Thus 1NF makes phone_no column's data atomic.

- Ensure that the table is already in First Normal Form (1NF). - Partial dependency occurs when a non-key attribute is dependent on only a part of a composite primary key, rather than on the whole key. - Determine if the table has a composite primary key (a primary key composed of more than one column). - Find attributes that are not part of the primary key but are included in the table. These are your non-key attributes. - Analyze dependencies to see if any non-key attributes depend on only part of the composite key. - Ensure that the new tables are related to the original table through foreign keys.

- Ensure that the table is already in Second Normal Form (2NF). - A transitive dependency occurs when a non-key attribute depends on another non-key attribute, rather than directly on the primary key. - Examine each non-key attribute to see if it depends on another non-key attribute rather than directly on the primary key. - Move the non-key attributes that are transitively dependent on another non-key attribute to a new table where the new table’s primary key is the attribute they depend on. - Verify that in the resulting tables, all non-key attributes are directly dependent on the primary key and not on other non-key attributes.

- Ensure that the table is already in Third Normal Form (3NF) - Determine all functional dependencies in the table - For each functional dependency X -> Y, verify if X is a candidate key. - Decompose it into smaller tables to ensure that all determinants are candidate keys - Ensure that the original table can be reconstructed from the decomposed tables without losing information (lossless join property) and that all original functional dependencies are preserved.