Convert ER Model to Relational Model.

In Database design, the ability to effectively model data is crucial for creating efficient and scalable systems. The Entity-Relationship (ER) Model serves as a foundational tool for representing data and its relationships in a structured manner. However, to implement these models in a relational database management system (RDBMS), it is essential to convert the ER Model into a Relational Model. 

This article will guide you through the process of converting an ER Model into a Relational Model, ensuring that you understand each step and its significance.

Let's start by understanding the ER Model and Relational Model.

What is the ER Model?

An Entity-Relationship (ER) Model is a conceptual framework used to describe the data and relationships within a system. It visually represents entities, their attributes, and the relationships between them, making it easier to understand the data structure.

Components of an ER Model

• Entities: These are objects or things in the real world that have a distinct existence. For example, in a university database, entities could include Student, Course, and Instructor.
• Attributes: Attributes are the properties or characteristics of entities. For instance, a Student entity may have attributes such as StudentID, Name, and DateOfBirth.
• Relationships: Relationships define how entities are related to one another. For example, a Student may enroll in multiple Courses, establishing a relationship between these two entities.

Significance of ER Models

ER Models play a vital role in database design by providing a clear and organized representation of data. They help stakeholders understand the data structure, facilitate communication among team members, and serve as a blueprint for database implementation.

What is a Relational Model?

A Relational Model is a way of structuring data in a database using tables (relations). Each table consists of rows (tuples) and columns (attributes), allowing for efficient data storage and retrieval.

Key Components of a Relational Model

• Tables (Relations): The primary structure in a relational database, where data is stored in rows and columns.
• Rows (Tuples): Each row in a table represents a single record or instance of the entity.
• Columns (Attributes): Each column represents a specific attribute of the entity, defining the type of data stored.

Importance of Relational Models

Relational Models are widely used in RDBMS due to their simplicity, flexibility, and ability to enforce data integrity through constraints. They allow for complex queries and efficient data manipulation, making them a popular choice for modern applications.

Steps to Convert ER Model to Relational Model

Step 1. Identify Entities and Attributes.

Entities are objects or things in the real world that have a distinct existence. In the ER Model, each entity is represented as a rectangle.

• Identify Entities: Review the ER diagram and list all the entities. For example, in a university database, entities might include Student, Course, and Instructor.
• Identify Attributes: For each entity, identify its attributes, which are the properties that describe the entity. Attributes are represented as ovals connected to their respective entities in the ER diagram. For example, the Student entity may have attributes like StudentID, Name, Address, and DateOfBirth.

Step 2: Define Primary Keys

A primary key is a unique identifier for each record in a table. It ensures that each tuple (row) in a relation (table) can be uniquely identified.

• Select Primary Keys: For each entity, determine the primary key based on the attributes. The primary key should be unique and not null. For example, StudentID can serve as the primary key for the Student entity.
• Consider Composite Keys: If an entity does not have a single attribute that can serve as a primary key, consider using a composite key, which is a combination of two or more attributes that together uniquely identify a record.

Step 3: Convert Relationships

Relationships in the ER Model define how entities are related to one another. The conversion of relationships into the Relational Model depends on the type of relationship:

3.1 One-to-One Relationship.

In a one-to-one relationship, each instance of one entity is associated with exactly one instance of another entity. This type of relationship is often used when two entities share a unique connection, and it can be represented in a relational database in two primary ways: by merging the entities into a single table or by creating separate tables with a foreign key.

Options for Representing One-to-One Relationships.

1. Merge into a Single Table:

• In this approach, both entities are combined into a single table. This is suitable when the attributes of both entities are closely related and often accessed together.

2. Separate Tables with Foreign Key:

• Alternatively, you can create two separate tables, with one table containing a foreign key that references the primary key of the other table. This approach is useful when the entities have distinct attributes or when you want to maintain a clear separation between them.

Example: Student and StudentProfile

Let’s consider an example involving a Student entity and a StudentProfile entity. Each student has exactly one profile, and each profile belongs to exactly one student.

Option 1: Merging into a Single Table

In this case, we can create a single Student table that includes all attributes from both entities:

Student Table:

StudentID (PK)	Name	DateOfBirth	Address	PhoneNumber
1	John Doe	2000-01-15	123 Main St	555-1234
2	Jane Smith	2004-04-25	456 Elm St	555-5678
3	Mohit Kr	1989-04-25	456 West Noida	999-5678

Option 2: Separate Tables with Foreign Key

In this approach, we create two separate tables: Student and StudentProfile. The StudentProfile table will have a foreign key referencing the StudentID from the Student table.

Student Table:

StudentID (PK)	Name	DateOfBirth
1	John Doe	2000-01-15
2	Jane Smith	2004-04-25
3	Mohit Kr	1989-04-25

StudentProfile Table:

ProfileID (PK)	StudentID (FK)	Address	PhoneNumber
1	1	123 Main St	555-1234
2	2	456 Elm St	555-5678
3	3	456 West Noida	999-5678

3.2 One-to-Many Relationships

In a one-to-many relationship, a single instance of one entity (the "one" side) is associated with multiple instances of another entity (the "many" side). This type of relationship is common in database design and is used to represent scenarios where one entity can have multiple related records in another entity.

Key Concept: Adding a Foreign Key

To implement a one-to-many relationship in a relational database, the primary key of the "one" side is included as a foreign key in the "many" side. This establishes a link between the two tables, allowing for efficient data retrieval and integrity.

Example: Student and Courses

Let’s consider an example where a Student can enroll in multiple Courses. In this scenario, each student can be associated with many courses, but each course enrollment is linked to only one student.

Entities:

• Student: Represents individual students.
• Course: Represents courses that students can enroll in.

Implementation:

• Student Table: This table will contain the details of each student, with StudentID as the primary key.
• Course Table: This table will contain the details of each course, with CourseID as the primary key. Additionally, it will include a StudentID foreign key to reference the Student table.

Student Table:

StudentID (PK)	Name	DateOfBirth
1	John Doe	2000-01-15
2	Jane Smith	2004-04-25
3	Alice Brown	2001-03-10

Course Table:

CourseID (PK)	CourseName	StudentID (FK)
101	Mathematics	1
102	Science	1
103	Literature	2
104	History	3
105	Computer Science	1

The Student table contains the primary key StudentID, which uniquely identifies each student.

The Course table contains a foreign key StudentID, which references the StudentID in the Student table. This establishes the one-to-many relationship, indicating that one student can enroll in multiple courses.

3.3 Many-to-Many Relationships

In a many-to-many relationship, multiple instances of one entity can be associated with multiple instances of another entity. This type of relationship is common in scenarios where entities have a reciprocal relationship, such as students enrolling in courses.

Key Concept: Creating a Junction Table

To effectively represent a many-to-many relationship in a relational database, you need to create a junction table (also known as a linking or associative table). This table serves as an intermediary that links the two entities together. The junction table will include the primary keys of both entities as foreign keys, establishing the relationship between them.

Example: Students and Courses

Let’s consider an example where Students can enroll in multiple Courses, and each Course can have multiple Students.

Entities:

• Student: Represents individual students.
• Course: Represents courses that students can enroll in.

Implementation:

• Student Table: This table contains the details of each student, with StudentID as the primary key.
• Course Table: This table contains the details of each course, with CourseID as the primary key.
• StudentCourse Junction Table: This table links the Student and Course tables, containing foreign keys referencing both StudentID and CourseID.

Student Table:

StudentID (PK)	Name	DateOfBirth
1	John Doe	2000-01-15
2	Jane Smith	2004-04-25
3	Alice Brown	2001-03-10

Course Table:

CourseID (PK)	CourseName
101	Mathematics
102	Science
103	Literature
104	History
105	Computer Science

StudentCourse Junction Table:

StudentID (FK)	CourseID (FK)
1	101
1	102
1	105
2	103
3	104
2	101

The StudentCourse junction table contains two foreign keys: `StudentID` and `CourseID`. This table establishes the many-to-many relationship by linking students to the courses they are enrolled in.

In the example, John Doe (StudentID 1) is enrolled in three courses: Mathematics (CourseID 101), Science (CourseID 102), and Computer Science (CourseID 105). Jane Smith (StudentID 2) is enrolled in Literature (CourseID 103) and Mathematics (CourseID 101). Alice Brown (StudentID 3) is enrolled in History (CourseID 104).

Step 4: Handle Attributes

Once the entities and relationships are defined, the next step is to assign attributes to the corresponding tables.

• Assign Attributes: Each attribute identified in the ER Model should be included in the corresponding table. For example, the Student table will have attributes like StudentID, Name, and DateOfBirth.
• Composite Attributes: If an attribute is composite (e.g., FullName can be split into FirstName and LastName), break it down into simpler attributes.
• Multi-Valued Attributes: If an attribute can have multiple values (e.g., a PhoneNumbers attribute), create a separate table to store these values, linking it back to the main entity.

Step 5: Normalize the Relational Model

Normalization is the process of organizing data to minimize redundancy and improve data integrity. It involves structuring the relational model into different normal forms.

5.1 First Normal Form (1NF)

• Atomic Values: Ensure that all attributes contain atomic (indivisible) values. Each column should hold a single value, and each record must be unique.

5.2 Second Normal Form (2NF)

• Eliminate Partial Dependencies: Ensure that all non-key attributes are fully functionally dependent on the primary key. If any non-key attribute depends only on a part of a composite key, separate it into a new table.

5.3 Third Normal Form (3NF)

• Remove Transitive Dependencies: Ensure that non-key attributes are not dependent on other non-key attributes. If a non-key attribute depends on another non-key attribute, create a new table to eliminate this dependency.

Summary of Steps

• Identify Entities and Attributes: List all entities and their attributes from the ER Model.
• Define Primary Keys: Select unique primary keys for each entity.
• Convert Relationships: Transform relationships into foreign keys or junction tables based on their types.
• Handle Attributes: Assign attributes to tables, breaking down composite and multi-valued attributes as necessary.
• Normalize the Relational Model: Apply normalization principles to ensure data integrity and reduce redundancy.

By following these detailed steps, you can effectively convert an ER Model into a Relational Model, laying the groundwork for a well-structured and efficient database. This process is essential for ensuring that the database can handle data effectively while maintaining integrity and minimizing redundancy.

Relational Algebra in DBMS.

Relational Algebra is a theoretical language used to query and manipulate relational databases. It serves as the foundation of SQL and is crucial for understanding how database queries are executed internally. By learning relational algebra, database users and developers gain a deeper understanding of how data is filtered, combined, and retrieved efficiently.

What is Relational Algebra in DBMS?

Relational Algebra is a procedural query language that takes relations (tables) as input and returns new relations as output. Unlike SQL, which describes what you want (declarative), relational algebra describes how to get it (procedural). It consists of a set of operations that allow the combination, filtering, and transformation of data from relational tables.

Relational Algebra is fundamental in query optimization and database engine design, forming the mathematical backbone of relational databases.

Why is Relational Algebra Important?

Relational Algebra is a fundamental concept in Database Management Systems (DBMS) for several reasons:

• Foundation of Query Languages: Relational Algebra serves as the theoretical foundation for SQL (Structured Query Language), which is the most widely used language for querying and manipulating relational databases. Understanding relational algebra helps in grasping how SQL operates.
• Formalism: It provides a formal framework for defining and manipulating data. This formalism allows for precise definitions of operations on relations (tables), which is crucial for database design and optimization.
• Set Operations: Relational Algebra includes a variety of operations such as selection, projection, union, intersection, and difference, which are essential for querying relational databases. These operations allow users to retrieve and manipulate data in a structured way.
• Optimization: Understanding relational algebra helps database administrators and developers optimize queries. By analyzing the algebraic expressions, one can determine the most efficient way to execute a query, which is vital for performance in large databases.
• Data Independence: Relational Algebra supports the concept of data independence, allowing users to interact with data without needing to understand the underlying physical storage. This abstraction simplifies data management and enhances usability.
• Relational Model: It is integral to the relational model of data, which organizes data into tables (relations) and defines relationships between them. This model is widely adopted due to its simplicity and effectiveness in representing complex data relationships.

Basic Operations of Relational Algebra.

Relational Algebra consists of a set of fundamental operations that can be performed on relations (tables) in a relational database. These operations allow users to manipulate and query data effectively. The basic operations of Relational Algebra are as follows:

1. Selection (σ): The selection operation retrieves rows from a relation that satisfy a specified condition. It is denoted by the sigma (σ) symbol.

Example: To select all employees with a salary greater than $50,000 from the Employees table: 

2. Projection (π): The projection operation retrieves specific columns from a relation, effectively reducing the number of attributes. It is denoted by the pi (π) symbol.

Example: To retrieve only the names and salaries of employees from the Employees table: 

3. Union (∪): The union operation combines the tuples of two relations, eliminating duplicates. Both relations must have the same number of attributes and compatible data types.

Example: To combine the Employees and Contractors tables: 

4. Difference (−): The difference operation retrieves tuples that are present in one relation but not in another. It is denoted by the minus (−) symbol.

Example: To find employees who are not contractors: 

5. Cartesian Product (×): The Cartesian product operation combines every tuple of one relation with every tuple of another relation, resulting in a new relation with all possible combinations.

Example: To combine the Employees and Departments tables: 

6. Join (⨝): The join operation combines tuples from two relations based on a related attribute. There are several types of joins, including inner join, outer join, and natural join.

Example: To join the Employees table with the Departments table on the DepartmentID attribute:

$\text{Employees}{\bowtie }_{\text{Employees.DepartmentID}=\text{Departments.DepartmentID}}\text{Departments}$

In addition to the basic operations, relational algebra also includes advanced operations such as:

• Intersection (∩): Retrieves tuples that are present in both relations.
• Division (÷): Used to find tuples in one relation that are related to all tuples in another relation.

Conclusion

Relational algebra is a fundamental concept in database management systems, providing a formal framework for querying and manipulating relational data. Its operations allow users to perform a wide range of data retrieval and manipulation tasks, forming the basis for SQL and other query languages. Understanding relational algebra is essential for database professionals, as it enhances their ability to design efficient queries and optimize database performance. As databases continue to evolve, the principles of relational algebra remain relevant in the field of data management.

Also Read: Relational Algebra To SQL Translation.

Difference Between INNER JOIN and OUTER JOIN.

In Relational databases, the ability to combine data from multiple tables is crucial for effective data analysis and reporting. SQL (Structured Query Language) provides several types of joins to facilitate this process, with the most commonly used being INNER JOIN and OUTER JOIN. Understanding the differences between these two types of joins is essential for anyone working with databases.

What is JOIN in SQL?

In SQL, a JOIN is a powerful operation that allows you to combine rows from two or more tables based on a related column between them. Joins are essential for querying data from multiple tables in a relational database, enabling you to retrieve meaningful information that spans across different entities.

There are several types of JOINs in SQL, each of which serves a different purpose:

• INNER JOIN.
• LEFT JOIN (or LEFT OUTER JOIN).
• RIGHT JOIN (or RIGHT OUTER JOIN).
• FULL JOIN (or FULL OUTER JOIN).
• CROSS JOIN.

What is an INNER JOIN in SQL?

An INNER JOIN in SQL is a type of join that retrieves records from two or more tables where there is a match between the specified columns. It returns only the rows that have corresponding values in both tables, effectively filtering out any rows that do not meet the join condition. This makes INNER JOIN a powerful tool for combining related data from different tables in a relational database.

Syntax of INNER JOIN

The basic syntax for an INNER JOIN is as follows:

SELECT columns
FROM table1
INNER JOIN table2
ON table1.common_column = table2.common_column;

• SELECT columns: Specifies the columns you want to retrieve from the tables.
• FROM table1: Indicates the first table from which to retrieve data.
• INNER JOIN table2: Specifies the second table to join with the first table.
• ON table1.common_column = table2.common_column: Defines the condition for the join, specifying the columns that should match between the two tables.

Example of INNER JOIN.

Customers

CustomerID	CustomerName	Country
1	Alice	USA
2	Bob	UK
3	Charlie	Canada

Orders

OrderID	CustomerID	OrderDate
101	1	2023-01-15
102	2	2023-02-20
103	1	2023-03-10
104	4	2023-04-05

To retrieve a list of customers along with their orders, you can use an INNER JOIN:

 

SELECT Customers.CustomerName, Orders.OrderID, Orders.OrderDate
FROM Customers
INNER JOIN Orders
ON Customers.CustomerID = Orders.CustomerID;

Result:

CustomerName	OrderID	OrderDate
Alice	101	2023-01-15
Bob	102	2023-02-20
Alice	103	2023-03-10

In this example, the result set includes only the customers who have placed orders. Charlie is excluded because there are no matching records in the Orders table for CustomerID 3. Similarly, the order with CustomerID 4 is not included because there is no corresponding customer in the Customers table.

Key Points

• Filtering: INNER JOIN filters out rows that do not have matching values in both tables.
• Multiple Joins: You can perform multiple INNER JOINs in a single query to combine data from more than two tables.
• Performance: INNER JOINs are generally efficient, especially when the join columns are indexed.

What is an OUTER JOIN in SQL?

An OUTER JOIN in SQL is a type of join that retrieves records from two or more tables, including those that do not have matching values in both tables. Unlike an INNER JOIN, which only returns rows with matching values, an OUTER JOIN includes all rows from one table and the matched rows from the other table. If there is no match, NULL values are returned for the columns of the table that do not have a corresponding match.

Types of OUTER JOIN.

There are three main types of OUTER JOINs:

1. LEFT OUTER JOIN (or LEFT JOIN): Returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for columns from the right table.

Example of LEFT OUTER JOIN:

Consider the same Customers and Orders tables from the previous examples:

Customers

CustomerID	CustomerName	Country
1	Alice	USA
2	Bob	UK
3	Charlie	Canada

Orders

OrderID	CustomerID	OrderDate
101	1	2023-01-15
102	2	2023-02-20
103	1	2023-03-10
104	4	2023-04-05

To retrieve a list of all customers along with their orders (including customers who have not placed any orders), you can use a LEFT OUTER JOIN:

SELECT Customers.CustomerName, Orders.OrderID
FROM Customers
LEFT OUTER JOIN Orders
ON Customers.CustomerID = Orders.CustomerID;

Result:

Customers and Orders

CustomerName	OrderID
Alice	101
Bob	102
Alice	103
Charlie	NULL

In this result, Charlie is included even though he has not placed any orders, with a NULL value in the OrderID column.

2. RIGHT OUTER JOIN (or LEFT JOIN): RIGHT OUTER JOIN (or RIGHT JOIN): Returns all rows from the right table and the matched rows from the left table. If there is no match, NULL values are returned for columns from the left table.

Example:

SELECT Customers.CustomerName, Orders.OrderID
FROM Customers
RIGHT OUTER JOIN Orders
ON Customers.CustomerID = Orders.CustomerID;

3. FULL OUTER JOIN (or FULL JOIN): Returns all rows when there is a match in either the left or right table records. If there is no match, NULL values are returned for the columns of the table that do not have a match.

Example:

SELECT Customers.CustomerName, Orders.OrderID
FROM Customers
FULL OUTER JOIN Orders
ON Customers.CustomerID = Orders.CustomerID;

Difference Between INNER JOIN and OUTER JOIN.

Below is a tabular representation of INNER JOIN and OUTER JOIN:

INNER JOIN	OUTER JOIN
Returns only rows with matching values in both tables.	Returns all rows from one table and matched rows from the other, with NULLs for non-matches.
Includes only records that have matches in both tables.	Includes all records from one table plus matched records from the other table.
Single type: INNER JOIN.	Three types: LEFT OUTER JOIN, RIGHT OUTER JOIN, FULL OUTER JOIN.
Retrieve records having relationships in both tables.	Include all records from one table, even if no matching records in the other.
Generally faster, returns only matching rows.	May be slower due to additional NULL handling and inclusion of unmatched rows.
Example Syntax:- SELECT * FROM table1 INNER JOIN table2 ON condition;	Example Syntax:- SELECT * FROM table1 LEFT OUTER JOIN table2 ON condition;

Understanding the differences between INNER JOIN and OUTER JOIN is fundamental for effective database querying. INNER JOIN is best suited for scenarios where only matching records are needed, while OUTER JOIN is essential when you want to include all records from one table, regardless of matches. 

Relational Algebra To SQL Translation.

Relational Algebra (RA) is the mathematical foundation of SQL. Understanding how to translate RA operations to SQL is crucial for query optimization, database design, and performance tuning. This guide covers all core RA operations, their SQL equivalents, and practical translation techniques.

What is Relational Algebra?

Relational Algebra is a formal system for manipulating relations (tables) in a relational database. It consists of a set of operations that take one or two relations as input and produce a new relation as output. The primary operations in relational algebra include:

• Selection (σ): Filters rows based on a specified condition.
• Projection (π): Selects specific columns from a relation.
• Union (∪): Combines the tuples of two relations, removing duplicates.
• Difference (−): Returns tuples that are in one relation but not in another.
• Cartesian Product (×): Combines every tuple of one relation with every tuple of another.
• Join (⨝): Combines tuples from two relations based on a related attribute.

Relational algebra provides a theoretical foundation for relational databases and is used to formally express queries.

What is SQL?

SQL (Structured Query Language) is a standard programming language used for managing and manipulating relational databases. It allows users to perform various operations such as querying data, updating records, inserting new data, and deleting existing data. SQL is widely used due to its simplicity and effectiveness in handling structured data.

Key components of SQL include:

• Data Query Language (DQL): Used for querying data (e.g., SELECT statements).
• Data Definition Language (DDL): Used for defining database structures (e.g., CREATE, ALTER, DROP).
• Data Manipulation Language (DML): Used for manipulating data (e.g., INSERT, UPDATE, DELETE).
• Data Control Language (DCL): Used for controlling access to data (e.g., GRANT, REVOKE).

Why is Translation Important?

The translation from Relational Algebra to SQL is important for several reasons:

1. Theoretical Foundation: Relational algebra provides a formal basis for understanding the operations that can be performed on relational data. SQL, being a practical implementation, is built upon these theoretical concepts.

2. Optimization: Understanding the relational algebra expressions allows database systems to optimize SQL queries. By translating SQL into relational algebra, the database engine can apply various optimization techniques to improve query performance.

3. Query Design: Knowledge of relational algebra helps database designers and developers to construct more efficient and effective SQL queries. It encourages a deeper understanding of how data is structured and accessed.

4. Interoperability: Many database systems support SQL, but the underlying principles of relational algebra remain consistent. This allows for easier migration and adaptation of queries across different systems.

5. Educational Value: Teaching relational algebra alongside SQL helps students and practitioners grasp the underlying principles of relational databases, leading to improved programming practices and more effective database design.

The translation from relational algebra to SQL bridges the gap between theoretical concepts and practical applications, enhancing both the performance and understanding of database operations.

How to Translate Relational Algebra to SQL?

Translating a relational algebra expression to an SQL query involves several steps. Here’s a structured approach to perform this translation, along with an example.

Steps to Translate Relational Algebra to SQL:

1. Identify the Relations: Determine the tables involved in the relational algebra expression.

2. Identify the Operations: Recognize the operations being performed (e.g., selection, projection, join, etc.).

3. Translate Selection (σ): Use the WHERE clause in SQL to filter rows based on the specified condition. Example:

• RA: σ_{Salary > 5000}(Employees)
• SQL: SELECT * FROM Employees WHERE Salary > 5000;

4. Translate Projection (π): Use the SELECT statement to specify the columns to be retrieved. Example:

• RA: π_{Name, Dept}(Employees)
• SQL: SELECT Name, Dept FROM Employees;

5. Translate Join (⨝): Use the JOIN clause to combine tables based on a related attribute. Example:

• RA: R ⋈ S (Natural Join)
• SQL: SELECT * FROM R JOIN S ON R.A = S.A;

6. Translate Union (∪): Use the UNION operator to combine results from two queries. Example:

• RA: Active_Employees ∪ Retired_Employees
• SQL: SELECT * FROM Active_Employees UNION SELECT * FROM Retired_Employees;

7. Translate Difference (−): Use a combination of SELECT and NOT IN or EXCEPT to find differences between two relations. Example: 

• RA: Students − Graduates
• SQL: SELECT * FROM Students EXCEPT SELECT * FROM Graduates;

8. Combine the Translations: Assemble the SQL query by combining the translated components in the correct order.

Example to Convert Relational Algebra to SQL Query:

Let's consider a simple example with two relations:

Students: Contains student information.

• Attributes: StudentID, Name, Major

Courses: Contains course information.

• Attributes: CourseID, CourseName, Instructor

Relational Algebra Expression

Suppose we want to find the names of students who are majoring in "Computer Science" and are enrolled in a course taught by "Dr. Smith". The relational algebra expression might look like this:

1. Selection: Select students majoring in "Computer Science".

$S1={\sigma }_{\text{Major}{=}^{\prime }ComputerScienc{e}^{\prime }}(\text{Students})$

2. Join: Join with the Courses table where the instructor is "Dr. Smith".

$S2=S1\bowtie {\sigma }_{\text{Instructor}{=}^{\prime }Dr.Smit{h}^{\prime }}(\text{Courses})$

3. Projection: Project the students' names.

${\pi }_{\text{Name}}(S2)$

Now, let's translate this into an SQL query:

SELECT s.Name
FROM Students s
JOIN Courses c ON s.StudentID = c.CourseID
WHERE s.Major = 'Computer Science' AND c.Instructor = 'Dr. Smith';

Explanation of the SQL Query

1. SELECT s.Name: This corresponds to the projection operation, where we want to retrieve the names of the students.

2. FROM Students s: This specifies the primary table (Students) and gives it an alias (s).

3. JOIN Courses c ON s.StudentID = c.CourseID: This represents the join operation, where we are joining the Students table with the Courses table based on a common attribute (assuming StudentID is related to CourseID for this example).

4. WHERE s.Major = 'Computer Science' AND c.Instructor = 'Dr. Smith': This corresponds to the selection operation, filtering the results to only include students majoring in "Computer Science" and enrolled in courses taught by "Dr. Smith".

By following these steps, you can systematically translate relational algebra expressions into SQL queries, ensuring that the logic and intent of the original query are preserved.

Data Models in DBMS.

Data Models in DBMS play an important role in organizing and managing data efficiently. A data model acts as a blueprint that defines how data is stored, connected, and accessed in a database. It bridges the gap between real-world information and how it is represented digitally, enabling better data management, consistency, and security.

What is a Data Model?

A Data Model in DBMS (Database Management System) is a conceptual framework that defines how data is structured, stored, connected, and manipulated within a database. It acts as a blueprint for building and organizing a database by providing rules and guidelines for how data should be represented.

Data models help users and developers understand the data and its relationships without worrying about how it is physically stored. They offer a high-level view of the data and ensure consistency, integrity, and clarity in database design. Data models also help in translating real-world scenarios into a structured database format.

Why Are Data Models Important in DBMS?

Data models are important because they help us organize data clearly and logically. They define how data is stored, related, and accessed, making it easier for developers, users, and database administrators to understand and manage the database. Without a data model, managing large amounts of data would be confusing and error-prone. A good data model ensures consistency, improves data quality, and supports better performance and scalability.

Key Purposes of a Data Model:

• It defines how data is stored, connected, and accessed.
• It helps in database design and communication among stakeholders.
• It enables data abstraction for various levels (user, logical, physical).
• It enhances data consistency, integrity, and security.

Types of Data Models in DBMS.

DBMS data models are broadly categorized into three main types:

• High-Level Data Models (Conceptual Models).
• Record-Based Data Models (Logical Models).
• Physical Data Models.

Let's discuss each of them one by one in complete detail with examples.

1️⃣ High-Level Data Models (Conceptual Models)

High-Level Data Models, also known as Conceptual Models, are used to describe what data is stored in the database and the relationships among those data without worrying about how the data is actually stored physically. These models focus on the meaning of the data and are often used during the initial design of a database.

They are easy to understand and help bridge the gap between technical database designers and non-technical stakeholders (like business users).

Key Characteristics:

• Focus on what data is needed, not how it is stored.
• Use simple diagrams to represent entities (like users or products) and their relationships.
• Hide low-level implementation details such as file formats or indexing.
• Often modeled using Entity-Relationship (ER) diagrams.

Example: 

Imagine you're designing a university database. In this case, the main entities would be Student, Course, and Instructor. The relationships between them could include a Student enrolling in multiple Courses and a Course being taught by one Instructor. 

These connections and entities are represented visually using an Entity-Relationship (ER) diagram, where rectangles denote entities, ovals represent attributes (such as student name or course name), and diamonds indicate relationships (like “enrolls in” or “teaches”). At this stage, the focus is on what data is important and how it logically connects, not on how it’s physically stored in the database.

2️⃣ Record-Based Data Models (Logical Models).

Record-Based Data Models, also known as Logical Data Models, describe how data is logically structured and how relationships between data are maintained. These models are called “record-based” because they represent data as fixed-format records of various types. Unlike conceptual models that focus on high-level design, logical models are more detailed and closer to how the data is actually stored in a database.

There are three main types of Record-Based Models:

• Relational Model
• Hierarchical Model
• Network Model

Let's discuss each of them in detail.

1. Relational Data Model: The relational model represents data in the form of tables (relations), where each table consists of rows (records) and columns (attributes). It uses primary keys and foreign keys to establish relationships between tables.

Example: Example: A customer table with customer details and an order table linked via customer ID.

2. Hierarchical Data Model: The hierarchical model organizes data in a tree-like structure with parent-child relationships. Each parent can have multiple children, but each child has only one parent. It is fast for read operations with a clear structure, but lacks flexibility for complex relationships.

Example: Example: An organization chart where a manager supervises multiple employees.

3. Network Data Model: This model represents data as a graph, allowing many-to-many relationships. It is more flexible than the hierarchical model, but can be complex to design and maintain.

Example: Students enrolled in multiple courses, and each course has multiple students.

3️⃣ Physical Data Model.

A Physical Data Model represents how data is actually stored in the computer system. It deals with the physical storage of data, including files, indexing, partitions, memory locations, and access paths. This model is closest to the hardware and focuses on optimizing performance, storage efficiency, and data retrieval speed.

While the Conceptual and Logical models focus on what data is stored and how it is logically related, the Physical Data Model focuses on how that data is actually written to disk and accessed efficiently.

Key Characteristics:

• Defines data storage structures like tables, indexes, and constraints.
• Specifies data types, column lengths, and storage format.
• Optimizes data access paths using techniques like indexing and clustering.
• Deals with database performance tuning, space allocation, and I/O optimization.

Example: In a physical data model, a Student table is stored on disk with an indexed StudentID and data types like VARCHAR(100) for names and INT for IDs to optimize storage and retrieval.

Which Model to Choose When?

Model	Best For	Avoid If
Relational	Structured data, complex queries	Unstructured data, high scalability
NoSQL	Big data, flexible schemas	ACID transactions, complex joins
Hierarchical	Tree-like data (e.g., file systems)	Many-to-many relationships
Network	Graph data (rarely used today)	Modern applications
Object-Oriented	OOP systems, multimedia data	High-performance needs

Final Recommendation:

• Use Relational for transactional systems (banking).
• Use NoSQL for scalability (social media, IoT).
• Use Hierarchical/Network only for legacy systems.

FAQs on Data Models in DBMS

Q1: Which is the most widely used data model in DBMS?

➡️ The Relational Model is the most commonly used due to its simplicity and support by modern RDBMS like MySQL and SQL Server.

Q2: What is the purpose of a conceptual data model?

➡️ It provides a high-level view of data for users and stakeholders, hiding implementation details.

Q3: Can a DBMS support multiple data models?

➡️ Yes, modern DBMS can support hybrid models like document and relational (e.g., PostgreSQL, MongoDB).

Conclusion.

Data models are the backbone of any database system. From high-level designs to physical storage, they provide structure, clarity, and flexibility. Whether you're designing a simple database or a large-scale system, understanding these models is key to efficient, scalable, and secure database development.

Relational Integrity Constraints

In a Relational Database Management System (RDBMS), integrity constraints are rules that enforce data accuracy, consistency, and reliability. These constraints ensure that the database remains error-free, even when multiple users insert, update, or delete data.

Without integrity constraints, databases could suffer from:

❌ Duplicate records

❌ Invalid relationships (e.g., an order without a customer)

❌ Inconsistent data (e.g., negative age values)

In this article, we’ll explore the types of relational integrity constraints, their importance, and real-world examples.

Before reading this article, you should read the following DBMS topics:

• DBMS Introduction.
• Data Models in DBMS.
• Types of Keys in DBMS.

What are Relational Integrity Constraints?

Relational Integrity Constraints are rules that ensure data in a database remains accurate, consistent, and reliable. They prevent invalid entries, broken relationships, or illogical data by enforcing conditions on tables, columns, and relationships.

For example, a primary key ensures each row is unique, a foreign key guarantees valid links between tables, and a check constraint blocks impossible values (like negative ages). These constraints act as "guardrails" to maintain data quality, whether during insertions, updates, or deletions.

Types of Relational Integrity Constraints.

There are four types of Relational Integrity Constraints in DBMS:

• Entity Integrity Constraint.
• Referential Integrity Constraint.
• Domain Integrity Constraint.
• Key Constraints (Uniqueness).

1. Entity Integrity Constraint.

Entity Integrity Constraint ensures that each row in a table is uniquely identifiable and never left empty. 

It enforces two key rules:

• Primary Key (PK) must be unique (no duplicate values).
• PK cannot be NULL (every row must have an identifier).

This constraint guarantees that every record in a table can be distinctly accessed and referenced, preventing data ambiguity.

Example: Consider an example of the Employees Table with three columns.

Employee_ID (PK)	Name	Department
101	Alice	HR
102	Bob	IT
103	Charlie	Finance

How Entity Integrity Works:

Every Employee_ID (PK) must be unique and non-NULL in the Employees table.

✅ Valid: Employee_ID 103 (Charlie) is unique and not empty

❌ Invalid:
- Duplicate ID (101 for two employees)
- NULL value in Employee_ID

2. Referential Integrity Constraint.

Referential Integrity Constraint ensures that relationships between tables remain valid by enforcing that any value in a foreign key (FK) column must match an existing primary key (PK) value in the referenced table or be NULL. This prevents orphaned records (e.g., an order linked to a non-existent customer) and maintains data consistency across related tables.

Key Points:

• Ensures only valid relationships exist (e.g., no "ghost" customers for orders).
• Use ON DELETE CASCADE to auto-delete child records when a parent is removed.
• Blocks insertions/updates that break table relationships.

Example: Consider a relation between the parent (Customer) and child (Order) tables.

Customers (Parent Table)

Customer_ID (PK)	Name
1	Alice
2	Bob

Orders (Child Table)

Order_ID	Customer_ID (FK)	Amount
101	1	99.99
102	2	50.00

How Referential Integrity Works:

Every Customer_ID in the Orders table must exist in the Customers table's Customer_ID column.

✅ Valid: Order 101 links to Customer 1 (Alice)

❌ Invalid: An order with Customer_ID=3 would be rejected (no such customer exists)

3. Domain Integrity Constraint.

Domain Integrity Constraint ensures that all values in a database column adhere to defined rules, such as data types, formats, ranges, or custom conditions. It restricts what data can be entered into a column, preventing invalid or illogical values.

Key Aspects of Domain Integrity

• Data Type Enforcement: Columns must contain values of a specific type (e.g., INT, VARCHAR, DATE).
• Value Range/Rules: Uses CHECK constraints to limit values (e.g., Age >= 18).
• Mandatory Fields: NOT NULL ensures critical columns are never empty.
• Default Values: DEFAULT provides fallback values if none are specified.

Example: Employee Table with Domain Constraints

Employee_ID (INT PRIMARY KEY)	Name (VARCHAR(50) NOT NULL)	Age (INT CHECK >=18 AND <=70)	Email (VARCHAR(100) UNIQUE)	Join_Date (DATE DEFAULT CURRENT_DATE)	Salary (DECIMAL(10,2) CHECK >0)
101	Alice	30	alice@example.com	2023-10-05	5000.00

How Domain Integrity Works:

Each column enforces specific data rules:

✅ Valid Example: All values meet their constraints

❌ Violation Examples:

• NULL Name: (102, NULL, 25, 'bob@example.com', '2023-10-06', 4500.00)
• Age < 18: (103, 'Charlie', 17, 'charlie@example.com', '2023-10-07', 3000.00)
• Duplicate Email/Negative Salary: (104, 'Dave', 45, 'alice@example.com', '2023-10-08', -2000.00)

Key Constraints Shown:

• NOT NULL - Required fields
• CHECK - Value range validation
• UNIQUE - No duplicate emails
• DEFAULT - Automatic current date

4. Key Constraints (Uniqueness).

Key Constraints (Uniqueness) in database management systems enforce the principle that certain columns or combinations of columns must contain unique values across all rows in a table. These constraints are fundamental for maintaining data integrity by preventing duplicate entries in critical fields. 

The two primary types of uniqueness constraints are primary keys and unique keys, each serving distinct but related purposes. A primary key represents the main unique identifier for each record, requiring both uniqueness and non-null values, while a unique key also mandates distinct values but typically allows for one null entry. These constraints are implemented through database declarations that create automatic checks during data insertion or modification, rejecting any operation that would violate the uniqueness rule.

Example: Employee Table Example

CREATE TABLE Employees (
    Employee_ID INT PRIMARY KEY,         -- Primary Key (Unique + NOT NULL)
    Email VARCHAR(100) UNIQUE NOT NULL,  -- Unique Key (No duplicates)
    Passport_Number VARCHAR(20) UNIQUE   -- Unique Key (Allows one NULL)
);

Employee_ID (PK)	Email (Unique)	Passport_Number (Unique)
101	alice@company.com	A12345678
102	bob@company.com	B87654321
103	charlie@company.com	NULL
104	dave@company.com	C55555555

Key Observations:

• The Employee_ID column is the primary key - all values are unique and non-null
• The Email column has a unique constraint - no duplicates allowed (and NOT NULL enforced)
• The Passport_Number column has a unique constraint, but allows one NULL value

Conclusion.

Relational integrity constraints are essential for maintaining accurate, consistent, and reliable databases. By implementing PKs, FKs, CHECK, and UNIQUE constraints, you ensure data quality while improving performance.

Types of Keys in DBMS.

In Database Management Systems (DBMS), keys are the backbone of data organization, retrieval, and relationship management. They ensure that databases remain structured, efficient, and free from redundancy. Whether you're a beginner learning SQL or an experienced developer optimizing a database, understanding the different types of keys in DBMS is crucial for designing robust and scalable systems.

Keys serve multiple purposes:

• Uniquely identifying records (e.g., a student ID in a university database).
• Establishing relationships between tables (e.g., linking orders to customers).
• Maintaining data integrity by preventing duplicate or inconsistent entries.

In this article, we’ll explore the various types of keys in DBMS, their roles, and real-world use cases.

Types of Keys in the Relational Model.

Here is the list of Keys that are present in the Relational Model:

• Primary Key (PK) – Uniquely identifies each row (e.g., Student_ID).
• Foreign Key (FK) – Links tables by referencing a PK (e.g., Order.Customer_ID).
• Candidate Key – A potential PK (e.g., Email if unique).
• Alternate Key – Unused candidate keys (e.g., SSN when ID is PK).
• Composite Key – A PK made of multiple columns (e.g., (Student_ID, Course_ID)).
• Super Key – A set of columns containing a candidate key (e.g., {ID, Name}).

Let's discuss each of them one by one in detail,

1. Primary Key (PK).

A Primary Key (PK) is a column (or set of columns) in a table that uniquely identifies each row. It enforces data integrity by ensuring:

• No duplicate values (all entries must be unique).
• No NULL values (every row must have a PK value).

Key Features

• Uniqueness: Guarantees no two rows have the same PK value. Example: Employee_ID in the Employees table.
• Irreducibility: Cannot remove any column from a composite PK without breaking uniqueness.
• Mandatory: Every table must have exactly one PK (but can have multiple candidate keys).

Example:

CREATE TABLE Students (
    Student_ID INT PRIMARY KEY,  -- PK column
    Name VARCHAR(50),
    Email VARCHAR(100) UNIQUE    -- Candidate key
);

When to use Primary Key (PK)?

Use the Primary Key when:

• You need to uniquely identify each row in a table, such as assigning a unique EmployeeID to every record in an Employees table.
• You want to enforce data integrity, ensuring no duplicate or NULL values exist in the key column (e.g., ProductID in a Products table).
• Creating relationships between tables, as it serves as the target for Foreign Keys in related tables (e.g., CustomerID in a Customers table referenced by Orders).
• Optimizing query performance, since databases automatically index PKs for faster searches and joins (e.g., filtering orders by OrderID).
• Working with frameworks or ORMs, as most require a PK for CRUD operations (e.g., Django models or SQLAlchemy).

2. Foreign Key (FK).

A Foreign Key (FK) is a column (or set of columns) in a table that references the Primary Key (PK) of another table. It enforces referential integrity, ensuring valid relationships between tables.

Key Properties

• Links Tables: Creates relationships (e.g., Orders.Customer_ID → Customers.Customer_ID).
• Allows NULLs: Optional relationships (unless constrained).
• Supports Cascading Actions: Automatic updates/deletes (e.g., ON DELETE CASCADE).

Example:

CREATE TABLE Orders (
    Order_ID INT PRIMARY KEY,
    Customer_ID INT REFERENCES Customers(Customer_ID)  -- FK
);

When to use a Foreign Key (FK)?

Use a Foreign Key when:

• You need to link two tables, such as connecting an Orders table to a Customers table via CustomerID.
• You want to enforce referential integrity, ensuring that every record in the child table (e.g., Orders) corresponds to a valid record in the parent table (e.g., Customers).
• You want to automate updates or deletions across related tables, such as deleting all orders when a customer is removed (using ON DELETE CASCADE).
• Modeling relationships like one-to-many (e.g., one customer can have many orders) or many-to-one (e.g., many orders belong to one customer).

3. Candidate Key.

A Candidate Key is a minimal set of columns in a table that can uniquely identify each row without any redundancy. It satisfies two critical properties:

• Uniqueness: No two rows can have identical values in the candidate key columns.
• Irreducibility: No subset of the key can uniquely identify rows (it's "minimal").

Key Features

• A table can have multiple candidate keys.
• One candidate key is chosen as the Primary Key (PK); others become Alternate Keys.
• Candidate Key cannot contain NULL values (to ensure row uniqueness).

Example

In a Students table:

• Student_ID (Unique) → Candidate Key
• Email (Unique) → Candidate Key
• {Name, Birthdate} (If unique) → Composite Candidate Key

Note: All Primary Keys are candidate keys, but not all candidate keys become Primary Keys!

4. Alternate Key.

An Alternate Key is a candidate key that is not selected as the Primary Key (PK) in a table. It retains all properties of a candidate key (uniqueness and minimality) but serves as a backup unique identifier.

Key Features

• Uniqueness: Guarantees no duplicate values in its columns.
• Non-NULL: Cannot contain NULL values (like all candidate keys).
• Secondary Role: Used when the PK is impractical for certain queries.

Example

In a Students table:

• Primary Key (PK): Student_ID
• Alternate Keys: Email or SSN (if both are unique and minimal).

CREATE TABLE Students (
    Student_ID INT PRIMARY KEY,  
    Email VARCHAR(100) UNIQUE NOT NULL,  -- Alternate Key  
    SSN CHAR(9) UNIQUE NOT NULL          -- Alternate Key  
);

Note: Alternate Keys are implicitly indexed in most databases, just like PKs!

5. Composite Key.

A Composite Key is a type of primary key that consists of two or more columns that together uniquely identify each row in a table. The individual columns may contain duplicate values, but their combination must be unique.

Key Characteristics:

• Multi-Column Uniqueness: The combination of columns must be unique.
• Mandatory Fields: None of the component columns can contain NULL values.
• Relationship Enforcement: Often used in junction tables for many-to-many relationships.

Example:

In an Order_Details table that links orders to products:

CREATE TABLE Order_Details (
    OrderID INT NOT NULL,
    ProductID INT NOT NULL,
    Quantity INT,
    PRIMARY KEY (OrderID, ProductID)  -- Composite Key
);

When to use a Composite Key?

• When you need to uniquely identify relationships between entities
• When a single column isn't sufficient for uniqueness (e.g., flight number + date)
• When you need to uniquely identify records across multiple dimensions

6. Super Key.

A Super Key is any combination of columns in a table that can uniquely identify each row. Unlike candidate keys, super keys may contain extra columns that aren't strictly necessary for uniqueness.

Key Characteristics:

• Uniqueness Guarantee: Must uniquely identify each record.
• Non-Minimal: May include additional columns beyond what's needed for uniqueness.
• Flexible Composition: Can be any size from one column to all columns in the table.

In a Students table with columns:

• Student_ID (unique)
• Email (unique)
• Name
• Birthdate

Possible super keys include:

• {Student_ID}
• {Email}
• {Student_ID, Name}
• {Email, Birthdate}
• {Student_ID, Name, Email, Birthdate}

Conclusion.

Keys ensure data integrity, efficient querying, and logical relationships between tables. From Primary Keys that uniquely identify records to Foreign Keys that link tables, each key type serves a distinct purpose. Candidate Keys offer alternative unique identifiers, while Composite Keys handle complex relationships. Super Keys provide flexibility in uniqueness, and Alternate Keys act as backup identifiers.

Understanding these keys helps in:

✔ Designing optimized and scalable databases

✔ Maintaining data accuracy through constraints

✔ Improving query performance with proper indexing

Codd's 12 Rules for Relational Databases.

When E.F. Codd introduced the Relational Model in 1970, he also defined a set of 12 rules (numbered 0 to 12) that any Relational Database Management System (RDBMS) must satisfy to be considered truly relational. These rules serve as the foundation of relational databases, ensuring data integrity, independence, and consistency across systems.

Codd's Rules in DBMS.

Let’s explore each rule in simple words with examples.

Rule 0 – Foundation Rule.

"For a system to be considered relational, it must use its relational capabilities exclusively to manage the database."

Explanation: This is the baseline rule. It means the system must fully support all relational principles to qualify as a true RDBMS. Partial implementation (e.g., supporting SQL but not integrity rules) isn't enough.

Rule 1 – The Information Rule.

"All information in a relational database is represented explicitly at the logical level and in exactly one way – by values in tables."

Explanation: Every piece of data, including table names, column names, and actual data, should be stored in rows and columns. No hidden data, no pointers.

Example: A student’s name, age, and ID are stored in a Students table, not embedded in application logic.

Rule 2 – Guaranteed Access Rule.

"Each data item must be logically accessible by using a combination of table name, primary key, and column name."

Explanation: You should be able to retrieve any value in the database using a simple query, without needing to know the underlying storage structure.

Example: To get a student's name:

SQL Query: SELECT Name FROM Students WHERE StudentID = 101;

Rule 3 – Systematic Treatment of Null Values.

"Null values (distinct from zero or empty string) are supported for representing missing or inapplicable information."

Explanation: Nulls must be consistently treated and distinguishable from other values. They represent unknown or not applicable data.

Example: If a professor doesn't have an office assigned yet, the Office field should be NULL.

Rule 4 – Active Online Catalog (Data Dictionary)

"The database description is represented at the logical level in the same way as ordinary data, so authorized users can use the same relational language to access it."

Explanation: Metadata (like schema, tables, and columns) should be stored in tables and be queryable.

Example: This returns a list of tables in many RDBMS systems.

SQL Query: SELECT * FROM INFORMATION_SCHEMA.TABLES;

Rule 5 – Comprehensive Data Sub-language Rule.

"A relational system may support several languages and various modes of terminal use. But there must be one well-defined language that supports all of the following: data definition, data manipulation, data integrity constraints, and transaction control."

Explanation: There should be one powerful language (like SQL) that can handle everything: DDL, DML, constraints, and transactions.

Rule 6 – View Updating Rule

"All views that are theoretically updatable must be updatable through the system."

Explanation: If it’s logically possible to update a view, the system should allow it.

Example:

CREATE VIEW StudentNames AS SELECT Name FROM Students;
UPDATE StudentNames SET Name = 'Jane' WHERE Name = 'Janet';

Note: Not all views are updateable (e.g., ones with JOIN, GROUP BY, etc.).

Rule 7 – High-level Insert, Update, and Delete

"The system must support set-level inserts, updates, and deletes."

Explanation: You should be able to perform operations on multiple rows at once, not just one record at a time.

Example:

UPDATE Employees SET Bonus = 1000 WHERE Department = 'Sales';

Rule 8 – Physical Data Independence

"Changes to the physical storage of data should not require changes to applications that access that data."

Explanation: If you move your database from HDD to SSD or change the indexing method, your SQL queries should still work the same way.

Rule 9 – Logical Data Independence

"Changes to the logical structure (tables, views) should not affect how users interact with data."

Explanation: Applications should continue working even if the logical layout of data is modified.

Example: Splitting one large Employee table into CurrentEmployee and FormerEmployee shouldn’t break your application if views are maintained properly.

Rule 10 – Integrity Independence

"Integrity constraints must be stored in the catalog and not in application code."

Explanation: Rules like PRIMARY KEY, FOREIGN KEY, and CHECK constraints should be enforced by the database, not external applications.

Rule 11 – Distribution Independence

"The user should not be aware of whether the database is distributed."

Explanation: Whether your data is stored in one server or spread across many locations (distributed DB), it should be transparent to users.

Rule 12 – Non-subversion Rule

"If a system provides low-level access, it should not bypass the integrity rules or constraints."

Explanation: Even if there’s a “back door” or admin-level access, it must not allow data corruption or constraint violations.

Codd’s 12 Rules were visionary guidelines that laid the groundwork for modern relational databases. While few systems meet all of them perfectly, most modern RDBMSs strive to comply with them as much as possible to ensure scalability, security, and data integrity.