What is Normalization?
Normalization is the organization of data using a set of rules called normal forms while designing a database. It helps improve data accuracy and integrity while reducing data redundancy and inconsistent dependency. It was developed by IBM researcher Edgar Frank Codd in the 1970s to increase data and relational clarity in a database. The process includes organizing data in tabular formats and defining relationships among them. Codd proposed the relational model of databases and introduced the Normal Forms. Most practical applications of database organization can be achieved using the Third Normal Form. But still, some dependencies could exist so in 1974, he was joined by Raymond F. Boyce to develop a stronger version of 3NF, the Boyce-Codd Normal Form.
Types of Normalization
The set of rules used to create a database are called ‘forms’, these help in measuring the level of normalization of an entity. The different types of Normalization Forms are as follows:
1. First Normal Form (1NF):
1NF divides the database into logical units called ‘tables’ consisting of unique values in each related field making it easy to search, filter, and sort the information. While normalizing a database for 1NF a Primary key i.e. a single column is allotted to each data category. It helps in the redevelopment of the raw database into a manageable record. The primary key may consist of a combination of columns and the set is known as Composite Key.
2. Second Normal Form (2NF):
2NF is the schema of further breaking down the tables based on the partial dependency of data on the primary key. The specific units have a full functional dependency that applies to a single column of Primary key. The entity must completely comply with relationship rules of 1NF to be considered for 2NF and there shouldn’t be any partial dependency. A table with a Composite Primary Key must be split into 2 to generate a foreign key. The foreign key will be the column that references the Primary Key of the other table.
3. Third Normal Form (3NF):
The objective of entities eligible for 3NF is to eliminate non-dependent data while addressing the update anomaly. The inconsistency of the database following an update is called transitive dependency. Removal of these transitive dependencies leads to normalization from 2NF to 3NF. This is the ideal form of normalization of almost all tables.
4. Boyce Code Normal Form (BCNF):
Redundancies arising from functional dependencies are resolved by 3NF but any anomalies arising from additional constraints are handled through BCNF, also known as 3.5NF. A 3NF table or relation without a transitive dependency is in BCNF.
5. Fourth Normal Form (4NF):
At the 4NF level there are no non-trivial multivalued dependencies other than a candidate key. A relation from a table in the BCNF, without multi-value dependency, only can be in the 4NF.
6. Fifth Normal Form (5NF):
5NF is also known as project-join normal form (PJ/NF). It reduces redundancy in relational databases by isolating semantically related multiple relationships. For a table to be in 5NF its non-trivial join dependency should be implied by candidate keys.
7. Domain/Key Normal Form (DKNF):
DKNF is a stricter normal form than 5NF and it removes any additional type of dependencies and constraints. The main requirements for a 5NF to qualify for DKNF are that each constraint on the table should be a logical consequence and non-existence of all constraints other than domain and keys. Also, there shouldn’t be any insert or delete anomalies in the database. Specifying general integrity constraints is tough so the practical use of DKNF relation is limited.
8. Sixth Normal Form (6NF):
6th normal form is not a standardized form but a table eligible for 5NF only can qualify for 6NF. To be in the 6NF a relation should not contain any non-trivial join dependencies. It is stricter and less redundant that DKNF. The relational variables of entities in this form become irreducible components.
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Importance of Database Normalization
Normalization of operational data stores (ODSs) and data warehouses (DWs) helps in the following ways:
1. Consistency: As all information is stored in a single place, any chances of inconsistency are ruled out.
2. Object-to-data mapping: Normalized data schemas help with object-oriented goals.
3. Flexibility: Data values can be easily added to rows.
4. Accessibility: Normalized data can be easily accessed, processed, and understood.
5. Uniqueness: Data redundancy is minimized.
Advantages of Normalization
Database Normalization is used to design an organized and managed database to maintain accuracy and enhance productivity. The main advantages of normalizing a database are:
- Organization of the database through normalization improves data accuracy and reduces redundant data.
- Data consistency and flexibility improves the logical usage of data.
- Enhanced database security.
- All necessary functional dependencies are handled during the normalization process.
- Makes Index searching easier as the indexes tend to be narrow and short.
What is TSQL?
TSQL is an abbreviation for Transact-SQL or T-SQL. It is a set of proprietary extensions to SQL (Structured Query Language) created by Sybase and owned by Microsoft since 1987. This procedural language expands the Microsoft SQL Server standard with extra features such as declared variables, transaction control, stored procedures, error and exception handling, triggers, string operations, etc. TSQL is used to operate SQL server-based relational databases. It is easier to understand and Turing complete. All interactions with a SQL Server through an application are carried out by T-SQL.
The dominant features of TSQL are:
1. It is a procedural programming language used to create applications.
2. Generates compact and readable codes that are less vulnerable.
3. Support functions for string processing, date and time processing, and mathematics operations.
4. Availability of user-defined custom functions.
5. Offers developers flexible control over the application flow through local variables.
TSQL Functions
Functions can be defined using TSQL beyond the built-in functions of SQL Server.
There are four types of T-SQL functions:
Aggregate functions:
These deterministic functions operate on a collection of values to calculate one summary value. The values of multiple rows are submitted as input to obtain a more significant value.
Ranking functions:
These are nondeterministic functions that return a ranking value for every row in a partition. The ranks for rows with the same values will be the same.
Rowset functions:
These nondeterministic functions return an object that can be used as a view or table reference in SQL statements. Their results may vary against the same set of input values.
Scalar functions:
These user-defined functions operate on a single value and return a single value. It helps in simplifying a code but cannot be used to update data.
Analytical functions:
These functions support TSQL to perform complex tasks and enable expression of common analysis such as ranking, percentiles, moving averages, and cumulative sums in a single SQL statement.
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Differences between SQL and T-SQL
The differences between SQL and T-SQL are:
- SQL is an open-format programming language that works for various data providers and TSQL is its proprietary extension designed specifically for Microsoft SQL Server.
- SQL is used for implementing reporting techniques while TSQL is useful for the installation of Microsoft SQL servers using applications.
- SQL is a data-oriented language as it operates over data sets while TSQL is a transactional language.
- SQL can process basic queries but TSQL can be used to create applications and add services to them.
- At a given time only a single statement can be processed using SQL while a load of statements can be processed using different control and iteration structures of T-SQL.
- SQL can be embedded into TSQL but the vice versa isn’t possible.
- Unlike SQL, TSQL is Turing complete and more robust.
- Unlike SQL, T-SQL offers easy integration with Microsoft Business Intelligence tools like PowerBI.
Advantages of TSQL
TSQL helps in fast-paced development through better interaction with the SQL Server. The advantages of using TSQL are:
- TSQL offers modular programming and its extensions enhance its programmability.
- Increased reliability and proprietary security of the server.
- Efficient handling of sensitive data to reduce security threats.
- Minimizes traffic over the server while easily managing complex tasks.
- Allows incorporation of programming logic into the database.
- Provides better control over the database instance.
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Conclusion
Normalization aids in the easy organization of a database and TSQL assists in writing compact codes. Using these two concepts together makes the database and codes more readable and less vulnerable. The main areas of focus while using these will be designing tables as per the database architecture, reviewing and optimizing Query performance, and scaling the database by implementing it on the cloud. Using these in combination will help developers integrate Microsoft Business Intelligence for business analytics.
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