What is Data Normalization and Why Is It Important?
Normalization is the process of reducing data redundancy in a table and improving data integrity. Then why do you need it? If there is no normalization in SQL, there will be many problems, such as:
- Insert Anomaly: This happens when we cannot insert data into the table without another.
- Update Anomaly: This is due to data inconsistency caused by data redundancy and data update.
- Delete exception: Occurs when some attributes are lost due to the deletion of other attributes.
So normalization is a way of organizing data in a database. Normalization involves organizing the columns and tables in the database to ensure that their dependencies are correctly implemented using database constraints. Normalization is the process of organizing data in a proper manner. It is used to minimize the duplication of various relationships in the database. It is also used to troubleshoot exceptions such as inserts, deletes, and updates in the table. It helps to split a large table into several small normalized tables. Relational links and links are used to reduce redundancy. Normalization, also known as database normalization or data normalization, is an important part of relational database design because it helps to improve the speed, accuracy, and efficiency of the database.
Now the is a question arises: What is the relationship between SQL and normalization? Well, SQL is the language used to interact with the database. Normalization in SQL improves data distribution. In order to initiate interaction, the data in the database must be normalized. Otherwise, we cannot continue because it will cause an exception. Normalization can also make it easier to design the database to have the best structure for atomic elements (that is, elements that cannot be broken down into smaller parts). Usually, we break large tables into small tables to improve efficiency. Edgar F. Codd defined the first paradigm in 1970, and finally other paradigms. When normalizing a database, organize data into tables and columns. Make sure that each table contains only relevant data. If the data is not directly related, create a new table for that data. Normalization is necessary to ensure that the table only contains data directly related to the primary key, each data field contains only one data element, and to remove redundant (duplicated and unnecessary) data.
The process of refining the structure of a database to minimize redundancy and improve integrity of database is known as Normalization. When a database has been normalized, it is said to be in normal form.
Basic normal forms:
A relation is considered in 1NF if every domain attribute consists of one atomic or indiscreet value. it’s a really important property of a relationship that’s accessible within the RDBMS.
A relation is in 2NF only if it is in 1NF also as all the non-key attributes in the tables are addicted to the table’s primary key. this type applies to those relations that are connected using composite keys. The relation of the tables is coupled with a key that is composed of over one attribute.
A relation is in 3NF if and only if it’s in 2NF and there’s no transition dependency.
The main use of normalization is to utilize in order to remove anomalies that are caused because of the transitive dependency. Normalization is to minimize the redundancy and remove Insert, Update and Delete Anomaly. It divides larger tables into smaller tables and links them using relationships.
There are many benefits to normalizing a database. Some of the main advantages are:
- By using normalization redundancy of database or data duplication can be resolved.
- We can minimize null values by using normalization.
- Results in a more compact database (due to less data redundancy/zero).
- Minimize/avoid data modification problems.
- It simplifies the query.is
- The database structure is clearer and easier to understand.
- The database can be expanded without affecting existing data.
- Finding, sorting, and indexing can be faster because the table is small and more rows can be accommodated on the data page.
We can now see that the concepts of denormalization, normalization, and denormalization are technologies used in databases and are differentiable terms. Normalization is a method of minimizing insertion, elimination and update exceptions by eliminating redundant data. The reverse normalization process, which adds redundancy to the data to improve application-specific performance and data integrity.
|Concept||Normalization is the process of creating a general scheme for storing non-redundant and consistent data.||A process of combining the data so that it can be queried speedily is known as denormalization.|
|Goal||Reduce data redundancy and inconsistency.||Execute queries faster through introducing redundancy. |
|Used in||OLTP system and its focus is to speed up the insertion, deletion, and update of abnormalities and the preservation of quality data.||OLAP system, focusing on better search and analysis.|
|Data integrity||Here data integrity is well maintained.||Here data integrity may not retain.|
|Redundancy||Normalization eliminated redundancy.||Denormalization added redundancy.|
|Number of tables||In normalization number of tables increases.||Whereas denormalization decreases tables.|
|Disk space||Optimized usage of disk space is possible.||Whereas in denormalization optimal use of disk space is not possible.|
When to normalize data Normalization is particularly important for OLTP systems, where insert, update and delete operations are fast and are usually initiated by the end-user. On the other hand, normalization is not always seen as important for OLAP systems and data warehouses. Data is usually denormalized to improve the performance of queries that need to be run in that context.
When to denormalize data It is best to denormalize a database in several situations. Many data warehouses and OLAP applications use denormalized databases. The main reason for this is performance. These applications are often used to perform complex queries. Joining many tables usually returns very large records. There may be other reasons for database denormalization, for example, to enforce certain restrictions that may not be enforced.
Here are some common reasons you might want to denormalize your database:
- The most common queries require access to the entire concatenated data set.
- Most applications perform a table scan when joining tables.
- The computational complexity of the derived column requires an overly complex temporary table or query.
- You can implement constraints (based on DBMS) that could not otherwise be achieved
Although normalization is generally considered mandatory for OLTP and other transactional databases, it is not always appropriate for some analytical applications.