5 Different terminologies used in DBMS and their meanings
A Database Management System (DBMS) is a software system that allows users to create, manage and access databases. To better understand how DBMS work, it is important to be familiar with the different terminologies used in the field. In this article, we will explore five different terminologies that are commonly used in DBMS and explain their meanings.
1. Cardinality in DBMS
Cardinality in Database Management System refers to the relationship between the number of entities in one table and the number of entities in another table. In other words, it is a measure of the number of unique values in a column or a set of columns. There are two types of cardinality: High cardinality, which refers to the number of unique values in a column, and low cardinality, which refers to the number of duplicate values in a column. High cardinality columns are often used as primary keys, while low cardinality columns are often used as foreign keys.
2. Indexing in DBMS
Indexing in DBMS refers to the process of creating a separate data structure that stores a subset of the data in a table, along with a pointer to the location of the data in the table. It is a technique used to improve the performance of a database by reducing the number of data pages that need to be searched when querying the database. Indexing allows for faster data retrieval, especially in large databases where data retrieval can be slow without the use of indexes.
Indexing is a critical concept in DBMS as it allows the database to quickly locate and retrieve specific data. When a query is executed, the database uses the index to quickly locate the relevant data, reducing the need to search through all the data pages. This improves the performance of the database and makes it more efficient.
There are several types of indexes that can be used in DBMS:
- Clustered indexes: These indexes determine the physical order of data in a table. Each table can only have one clustered index.
- Non-clustered indexes: These indexes are separate from the data and point to the location of the data in the table. Each table can have multiple non-clustered indexes.
- Full-text indexes: These indexes are used for searching large text fields and improve the performance of text-based searches.
3. Data Abstraction in DBMS
Data abstraction in DBMS refers to the process of hiding the internal details of the database from the users. It provides a simplified view of the database and only displays the information that is relevant to the user. There are three levels of data abstraction in DBMS: Physical level, Logical level, and view level. The physical level is the lowest level, which refers to the actual storage of the data. The logical level is the next level, which refers to the organization and structure of the data. The view level is the highest level, which refers to the data as it is presented to the user.
4. Normalization in DBMS
Normalization is a database design technique used to organize tables in a relational database in such a way that data is stored in a way that minimizes data redundancy and dependency, and maximizes data integrity. The goal of normalization is to create a set of database tables that are well-structured, easy to maintain, and easy to query.
There are several normal forms that a table can conform to, each with a specific set of rules. The most commonly used normal forms are first normal form (1NF), second normal form (2NF), and third normal form (3NF).
First Normal Form (1NF) requires that all tables have a primary key, and that all data in the table is atomic, meaning that each cell contains only a single value. In other words, data should not be repeated within a table.
Second Normal Form (2NF) builds on 1NF and requires that all non-key columns in a table are functionally dependent on the primary key. This means that data in the non-key columns must be directly related to the primary key and cannot be determined by any other data in the table.
Third Normal Form (3NF) builds on 2NF and requires that all data in a table is not only functionally dependent on the primary key, but also that there is no transitive dependency between non-key columns. This means that data in the non-key columns cannot be determined by any other data in the table, other than the primary key.
In addition to these normal forms, there are other normal forms, such as Boyce-Codd Normal Form (BCNF), Fourth Normal Form (4NF), and Fifth Normal Form (5NF). However, these forms are typically less commonly used, as they tend to be more specific and not as widely applicable.
Normalization is an important aspect of database design, as it can help to ensure that data is stored in a consistent, efficient, and accurate manner. By following the rules of normalization, a database designer can create tables that are easy to update, query, and maintain.
It is also important to note that normalization itself may not be always suitable, In some case like NoSQL databases or Data warehousing, denormalization is used to improve performance. Therefore, in some cases, trade-offs need to be made between normalization and denormalization, depending on the specific requirements of the application and system.
5. ER Model in DBMS
ER Model in DBMS stands for Entity-Relationship Model, is a data modeling technique used to represent the relationships between different entities in a database. It describes the structure of a database in a graphical format and helps to identify the relationships between different data elements. An ER model is composed of entities, attributes, and relationships. Each entity represents a real-world object, such as a customer or a product, and each attribute represents a characteristic of the entity, such as a name or a price.
In conclusion, understanding the different terminologies used in DBMS is essential for the effective management and manipulation of data. Cardinality and data abstraction are important concepts that deal with the relationship between data tables and the level of detail that the users can access. Normalization, indexing, and ER model are other important concepts that are crucial to ensure data integrity, improve performance and organizing the data in a consistent manner. Familiarizing oneself with these concepts will help in gaining a deeper understanding.