Traditional Databases such as Hierarchical and Network Databases are unable to provide enough infrastructure to satisfy business requirements. The emergence of Relational Databases in the industry has helped companies to maintain their data more efficiently. Relational Databases deeply integrate with applications written in any programming language.
The Relational Data Model in RDBMS greatly helped businesses to enhance data usability. It uses logical relations to store and represent data in tables. With the help of SQL, it becomes easier to access and query data and write complex queries.
Before working with any Relational Database it is essential to have a good knowledge of the Relational Data Model in RDBMS. In this article, you will understand the Relational Data Model in RDBMS, the advantages, and disadvantages of using it, and the best practices you can follow while creating a Relational Data Model in RDBMS.
Table of Contents
What is RDBMS?
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RDBMS or a Relational Database Management System is a type of Database that stores data in tables having rows and columns so it can be used with other datasets. Relational Databases are capable of handling different types of data and complex queries with ease. Most organizations use RDBMS to store data instead of hierarchical Databases.
Relational Database Management System uses SQL Structured Query Language to access and manipulate data in the Database. All the popular Databases such as MS SQL Server, MySQL, Oracle, etc are RDBMS. It allows organizations and IT teams to create, update, administer, and interact with Databases.
Key Features of RDBMS
Some of the main key features of RDBMS are listed below:
- Data Dictionary: RDBMS offers data dictionaries and metadata collections that are used to manage and access data. It supports well-defined data structures and relationships programmatically.
- Data Storage Management: RDBMS is capable of storing the stored procedures that can be used again in the future and saves time.
- Extensions Support: RDBMS comes with extension support that allows connecting Databases with applications and pairs SQL language with other programming languages such as Java, .NET, Python, etc.
- Data Integrity: RDBMS ensures data integrity using complex algorithms while multiple users are using the Databases concurrently.
- Security: RDBMS features policy-based access controls to protect data while used by many users of the company with different data access rights.
What is a Relational Model?
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A Relational Data Model in RDBMS is the primary data model which represents how data is stored in Relational Databases. Earlier, every application stored data in its unique structure that is inefficient to understand every time the developer wants to use that data.
It is hard to maintain and optimize. But with the help of the Relational Data Model in RDBMS, the problem of multiple arbitrary data structures is solved.
The Relational Data Model in RDBMS stores data in the form of relations (tables) which provides a standard and simpler way to represent and query data that can be used by any application.
Tables in the Relational Data Model in RDBMS are efficient, intuitive, and flexible ways to store and access structured data in the Databases.
Also, SQL language deeply merges with RDBMS to write and query data in the Database.
Key Concepts When Developing Relational Models
The Database consists of many components based on the Relational Data Model in RDBMS. The following components are listed below:
- Attribute: It contains the name of the columns in a particular table. Each attribute must have a domain. These are properties that define a relation.
- Relation: The rows and columns in the table are used to store a collection of data elements.
- Tuple: These are rows in the relation or table that contains a single record.
- Degree: The degree of the Relational Data Model in RDBMS is the total number of attributes in the table.
- Cardinality: The total number of rows present in the table is the Cardinality of the Relational Model.
- Column: It represents the set of values for a specific attribute.
- Relation Key: Every row can have one or more than one attribute which is called a relation key. There are 6 different types of Relation keys, listed below.
- Candidate Key
- Super Key
- Composite Key
- Primary Key
- Alternate Key
- Foreign Key
- Attribute Domain: Each attribute has a pre-defined value and has its scope which is known as the attribute domain.
Different Types of Constraints in the Relational Model
The Relational Data Model in RDBMS follows some conditions to ensure the validation of a relation. The conditions that must hold for data present in the database for accuracy and accessibility are called Relational Integrity Constraints.
Before performing any operation such as insertion or deletion of data from a Database, or updating an existing value, a constraint check is done to maintain the data integrity.
The different constraints in Relational Data Model in RDBMS are listed below:
Domain Constraints
Domain Constraints specify that each attribute is bound to have a value that lies in a specific range of values. If there is no attribute value for the corresponding domain then it is not of the appropriate data type which violates the Domain Constraints.
According to Domain Constraints for every tuple, the value of its attribute must be unique. The supporting data types for Domain Constraints are integers, real numbers, characters, Booleans, variable length, strings, etc.
Key Constraints
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Key Constraints specify that every relationship must have an attribute or a set of attributes such that a tuple can be uniquely identified in the tuple. It can never be NULL and has to be unique.
Referential Integrity Constraint
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Referential Integrity Constraint in Relational Data Model in RDBMS is based on Foreign Key. It is defined between 2 interrelated tables.
According to Referential Integrity Constraint if a relation refers to a key attribute of a different or same table, then that key must exist in the given relation.
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Anomalies in Relational Data Models
An anomaly in the Relational Data Model in RDBMS is something different from the normal state or irregular than usual. There are 3 types of Database anomalies identified while designing a Database, listed below.
Insertion Anomaly
Insertion Anomaly occurs when a value tried to insert in a Database throws an error due to the absence of another attribute value.
Example:
If you try to insert marks of the student concerning the name of the student, it will throw an error if the student’s name is not present in the Database.
Deletion Anomaly
Deletion Anomaly occurs when data is automatically deleted, or data loss is caused by the deletion of any other data value related to the element.
Example:
From the Employee table that is related to the department table. If the department has one employee working under it and you try to delete the data of this employee from the table then there will be data loss related to the department also which can lead to data inconsistency.
Update Anomaly
Modification or Update Anomaly occurs when data redundancy or partial data updates leads to data inconsistency.
Example:
If the database contains duplicate values while updating the records and now data is stored redundantly then there will be data inconsistency.
Advantages of Relational Data Models
Some of the benefits of using the Relational Data Model in RDBMS are listed below.
- Easy-to-use and Simple: Relational Data Model in RDBMS makes it easier for users to store data in tables as compared to other data storage techniques such as hierarchical and network models.
- Independent Data Management Structure: The independent nature of the relations in the Relational Database Model allows users to easily manage and manipulate data which improves the performance of the Database.
- Scalable: Relational Data Model in RDBMS greatly supports scalability making it possible to enhance the usability of the Database.
- Query Capability: Users can use SQL language to access data and even easily write complex queries and avoid complex Database navigation.
Disadvantages of Relational Data Models
A few disadvantages of the Relational Data Model in RDBMS are listed below.
- Some Relational Databases have limits on the field lengths that cannot be changed and creates problems in storing data with long character length.
- The overall performance of the Relational Database depends on the number of relations present in the Database.
- As the number of tables in the Relational Database increases the need for more physical memory increases which also increases the cost of maintaining a Relational Database.
Best Practices for Creating Relational Data Models
A few best practices you can consider following while creating a Relational Data Model in RDBMS are listed below:
- Make sure that no 2 rows should be the same to avoid data redundancy.
- Store data in such a way that it can be represented as a collection of relations.
- The data stored in the rows should have data about the instances of an entity.
- Each cell of the table should hold a single value.
- Make sure that the attribute values should be from the same domain.
- Every column in the table should have a unique name.
Conclusion
In this article, you learned about the Relational Data Model in RDBMS and how it helps Relational Databases to organize data and access it. You also read about the advantages and disadvantages of using Relational Databases. Companies widely use Relational Databases for transaction data because of their high performance, scalability, and simplicity.
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