Introduction to Attributes in DBMS
Database management systems are software applications for managing and organising data. In order to make an efficient DBMS and manage it well, you need to understand attributes well.
Attributes are the characteristics or properties that describe each entity in the database, making them easier to manage and organise. Different types of attributes, such as single-valued, multi-valued, composite, null, and so on, play crucial roles in designing a database.
In this blog, we have discussed all of them and more, so if you want to know what is attribute in DBMS and its different types, keep on reading!
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What is an Attribute in DBMS?
In Database Management Systems (DBMS), attributes are referred to as the unique characteristics or properties of an entity in the system. An entity is a real-world object or concept; multiple entities can be associated with a relationship.
For example, in a database for students, the attributes used can be student name, student age, student id, student department, etc. Each attribute has a specific type of value and defines a specific characteristic about the entity. When storing data in a database table for students, each attribute will have its own designated column where the corresponding values will be stored.
Apart from that, attributes can also be used to define constraints on the data stored in the database. Supposedly, you are using an attribute that defines things like data type, length restrictions, and default values. Hence, such attributes will work as constraints and ensure that the data in the database is consistent and accurate.
Therefore, apart from defining the characteristics of an entity, attributes also play a crucial role in structuring the data in the database.
What is an entity in DBMS?
In simple terms, an entity is a piece of data stored in a database. It can be a person, event, place, or object. Each entity is defined by its attributes, which are the entity’s features or properties.
For example, if you have an entity called “Customer,” its properties could be “CustomerID,” “Name,” “Email,” and “Address.”
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Types of Attributes in DBMS
Attributes in DBMS can be classified into different types based on their specific implementations and requirements. Some of the common criteria of classification can be the number of values associated with them, the number of their sub-attributes, whether they are physically stored in the database or calculated based on other attributes, if they are a part of the primary key, and so forth.
Below are some of the main types of attributes in DBMS.
Single-Valued Attribute
A single-valued attribute in database is a type of attribute that can have only one value for each instance of an entity. In simple words, a single-valued attribute will have only one value per row in a table.
An example of a single-valued attribute would be the “student ID” in a student database since every student has only one name.
Single-valued Attribute in DBMS
This attribute plays a crucial role in database design as it provides a way to represent and organise data to reflect the underlying structure of the real-world entity. With the help of single-valued attribute data, you can manage and analyse data efficiently.
What is an instance in DBMS?
An instance of an entity defines the specific occurrence or the occurrence of the entity in the real world. For example, in a student database, each student will be an instance of the entity “student.”
Multi-Valued Attribute
As the name suggests, a multivalued attribute in DBMS is an attribute that has multiple values for a single instance of an entity. For multiple-valued attributes, you will have two or more values per row.
An example of such attributes will be the phone number of a student in a student database, author names in a book database, product tags (i.e., organic, vegan, gluten-free) in a product database and so on.
Learning about multi-valued attributes is important in database designing as it will help you easily represent more complex and flexible data models. However, managing and querying data from such attributes is more complex and requires thorough planning.
Composite Attribute
You can call an attribute a composite attribute in DBMS when it can be divided into sub-attributes, each representing a separate component of that attribute. It is also known as a compound attribute.
Like, the attribute “address” can be divided into sub-attributes like “house number,” “street name,” “city name,” “zip code,” etc. The attribute “Name” can also be divided into sub-attributes like “first name,” “middle name,” and “last name.”
Composite attributes help you design a more detailed database where you can run more specific searches and queries. However, while determining the sub-attributes, you must ensure they are related to the composite attribute but do not overlap any other sub-attribute.
Curious about the foundational elements of database management? Learn about the types of keys in DBMS that play a crucial role in organizing, accessing, and maintaining data efficiently.
Derived Attribute
Derived attributes can be calculated with the help of other attributes in the database. Therefore, a derived attribute must not be explicitly stored in the database, as you can calculate its value through other already present attributes.
For example, if you need to calculate a student’s age, you can use their date of birth (a single-valued attribute) and the present date.
Derived attributes help you in database designing by not taking up extra space. Instead of adding another row or column, you can calculate them on the go when needed.
Stored and Derived Attribute in DBMS
Stored Attribute
Stored attributes are explicitly stored in the database and maintained until they need to be updated or deleted. Their nature can be either simple or composite.
For example, in an employee database, the “name of the employee” is a stored attribute since it is explicitly stored until it needs to be updated or deleted. The same goes for the “address.”
In the case of products, the “price” or “quantity” can be the stored attribute as they are explicitly stored in the database, and their values are maintained until it is time to update or delete them.
Required Attributes
Required attributes, also known as mandatory attributes or non-null attributes, are attributes that must have a value assigned to them for every instance of an entity. This means that these attributes cannot be left empty or null.
While designing a database, a few attributes are assigned as required attributes so that we don’t miss out on essential data required for accurately representing information about an entity and do not end up with an incomplete or unreliable database.
Null Attribute
Null means empty or void. Hence a null attribute or optional attribute refers to an attribute in DBMS that does not have any value assigned to it or its value is unknown. You can use null attributes when there is missing information about an entity.
Like the attribute “middle name.” Not everyone has a middle name, so in that case, they can be made as null attributes. Or “date of death” in a people database, which can not be known until the person passes away.
They may seem insignificant at first, but they are important in designing a good database. However, ensure to handle them carefully, as they can affect the result accuracy.
Key Attribute
A key attribute in DBMS is a single or set of unique attributes that help identify each record in the table. It is also used to ensure data integrity and establish relationships between tables in the database.
For example, in a “Customers” table, the “Customer ID” is a unique identifier. Hence can be called a key attribute.
There are several types of key attributes, such as primary key, foreign key, candidate key, and so forth.
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Conclusion
This concise guide has provided you with an overview of attributes in DBMS and their in-depth functionalities. Without concrete knowledge about attributes, you can not design a well-functioning, accurate and reliable database.
Overall, a thorough understanding of DBMS features is required to develop and administer databases that match organisations’ demands and allow for optimal data utilisation.
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