“`html

Data Modeling is a methodology in Database Management System (DBMS) that arranges, structures, and delineates the data within the database, assisting in preserving the integrity and operational efficiency of the data. Data modeling presents a schematic for the database employing components that stipulate relationships and constraints. By clarifying the model, the user can envision how to build a database with enhanced performance and diminished redundancy. Numerous modeling techniques exist to optimize interaction among users, database administrators, and stakeholders. In this article, you will discover data modeling, its utilities, and their attributes.

Table of Contents:

• Diverse Types of Models in DBMS
• Principal Characteristics of Data Modeling Tools
• Data Modeling Tools in DBMS
• Selecting the Appropriate Data Modeling Tool
• Database Normalization
• Categories of Normal Forms in Database Normalization
• Overview of Normal Forms in DBMS
• Indexing in DBMS
• Varieties of Indexes in DBMS
• Contrasting Clustered and Non-Clustered Indexes
• Examples of Indexes in DBMS
• Final Thoughts

Diverse Types of Models in DBMS

The three principal models in DBMS are the conceptual model, logical model, and physical model.

Conceptual Model in DBMS

The conceptual model in DBMS serves as a high-level framework. This model does not delve into the technical specifics of the data. It primarily aids the user in grasping the fundamental concepts. The concepts are represented through attributes and connections using an Entity-Relationship (ER) diagram. The ER diagram proves beneficial for stakeholders to comprehend the hierarchy and layout of the data.

Illustration: If the database aims to manage banking data, the conceptual model will construct an Entity Relationship (ER) featuring the manager, transactions, accounts, and user IDs. This entity diagram enables stakeholders and users to grasp the layout of the database.

Example of ER:

+————-+ +————-+ +—————+

| Customer |——<| Account |——<| Transaction |

+————-+ 1:N +————-+ 1:N +—————+

|

| 1:N

v

+————-+ +————-+

| Loan | | Branch |

+————-+ +————-+

| 1:N

v

+————-+

| Employee |

+————-+

Logical Model in DBMS

The logical model in DBMS aids in determining the components present within the database. This model establishes primary keys, foreign keys, attributes, and the relationships among them. It ensures normalization and data integrity. The representation of data is in a tabular format, yet none of the entities will rely on a specific database.

Illustration: In a banking application, users possess unique user IDs and transaction IDs that link to their loans in a designated bank.

Diagram Representation:

+────────────+ +────────────+ +────────────────+

| CUSTOMER |◄───┐ | ACCOUNT |◄──────| TRANSACTION |

+────────────+ │ +────────────+ +────────────────+

▲ │ │ ▲ ▲

│ └────────┼─────┘ │

│ 1:N │ 1:N │ 1:N

▼ ▼ │

+────────────+ +────────────+

| LOAN | | BRANCH |───────────────────┘

+────────────+ +────────────+

“““html
│ 1:N

                    ▼

               +────────────+

               | EMPLOYEE  |

               +────────────+

Physical Schema in DBMS

The physical schema in DBMS is the concrete realization of the database. It illustrates to the user how the information is retained and organized within a database. The physical schema delineates and determines the specifics of table configurations, their storage locations, constraints that may be applied, table indexes, divisions of tables or data, and the types of information that can enhance the database’s efficiency. This schema converts the logical design into a functional schema with the assistance of SQL. SQL enhances query performance through indexing and efficient data storage techniques.

Example: MySQL excels in establishing a functional schema in a database.

Diagram representation:

+───────────────+

|   CUSTOMER    |

+───────────────+

| PK customer_id|

|    name       |

|    address    |

|    phone      |

|    email (UQ) |

|    ssn (ENC)  |

+───────────────+

       △ 1

       │

       │ * (1:N)

+───────────────+         +─────────────────+

|    ACCOUNT               |─────|   TRANSACTION   |

+────────────+                  +────────────────+

| PK account_id |                         *  | PK transaction_id|

| FK customer_id|        ◄──────┤ FK account_id    |

| FK branch_id  |                             |    amount             |

|    type       |                                    |   type                   |

|    balance    |                                 |    timestamp         |

|    status     |                             +────────────────+

+────────────+

       △ 1

       │

       │ 1 (1:1)

+───────────────+

|    BRANCH     |

+───────────────+

| PK branch_id  |

|    location   |

| FK manager_id |

+───────────────+

       △ 1

       │

       │ * (1:N)

+───────────────+

|   EMPLOYEE    |

+───────────────+

| PK employee_id|

| FK branch_id  |

|    name       |

|    salary(ENC)|

+───────────────+

       △ 1

       │

       │ * (1:N)

+───────────────+

|     LOAN       |

+───────────────+

| PK loan_id    |

| FK customer_id|

|    amount     |

|    interest   |

|    status     |

+───────────────+

Explanation: This will illustrate the diagrammatic representation of the banking framework with indexes and attributes of the tables. This serves as the operational schema for a banking system that ensures enhanced performance and data integrity.

Essential Features of Data Modeling Tools

1. Generating an ER Diagram

An Entity-Relationship (ER) diagram visually depicts data structures within a database. An ER diagram includes entities, their attributes, and the relationships among them. This diagram aids users in comprehending the design and connections of the data elements by visually mapping them. It is utilized to formulate a design strategy for an explicit understanding of data dependencies and flows.

2. Employing Schema

Utilizing a schema, the ER diagrams or conceptual models are transformed into a physical database schema, which serves as a model depiction. The schema will automatically create tables, keys, indexes, and relationships through SQL scripts. It operates dynamically, eliminating the need for users to manually execute queries. This guarantees consistency.

3. Reverse Engineering

Data modeling incorporates reverse engineering that enables users to generate or create models from existing data in the database. This process scrutinizes the data structure before producing a model based on that. Such an approach is beneficial for re-engineering existing models that lack clarity. Reverse engineering significantly aids in optimizing and enhancing the efficiency of the database, facilitating modifications and redesigns where necessary.

“““html

assist the enterprise in enhancing the database while maintaining its integrity and architecture.

4. Concurrent User and Version Management

The Concurrent User feature enables several individuals to collaborate on the same model at the same time without interfering with each other within the database. It records all logs of modifications, maintains version history, and allows users to revert to earlier versions if necessary. For expansive projects or databases, it cooperates with source control tools like Git to handle modifications.

5. Error Mitigation and Data Uniformity

The Data model guarantees that the constructed models adhere to the rules and integrity of the constraints. It will detect lost records, redundant relationships, and missing constraints that could result in discrepancies during execution. By following normalization standards and integrity constraints, such as primary keys, foreign keys, and unique constraints, these verification processes will ensure the model’s consistency and integrity, leading to a well-structured and understandable database.

6. Utilization of Alternative DBMS Software

Data Modelling facilitates the creation and management of databases using various database management systems (DBMS) such as Oracle, MySQL, SQL Server, PostgreSQL, and NoSQL environments. It offers great flexibility, allowing users to select databases that best suit their applications. It is so compatible that users can seamlessly transfer data between different databases when required.

7. Dynamic Document Generation

This feature will effortlessly produce log reports containing comprehensive details on modifications or changes made, database architecture, schema, relationships among data elements, and indexing strategies in a report format. This will simplify the understanding of database strategies or model drafts for developers, administrators, and stakeholders without needing technical expertise. It is particularly beneficial for conducting audits and ensuring compliance. It guarantees that all updates are meticulously documented throughout the application development process.

8. Performance Enhancement and Indexing

This feature will offer recommendations to users for effective indexing through partitioning and query optimization. It will assess the relationships between entities and the strategies drafted to execute the model to enhance database performance. It will eliminate duplicates in the tables, allowing for more efficient data retrieval.

9. Integration with Development and Deployment Tools in DBMS

This facilitates smoother integration through CI/CD pipelines, Database Management Systems (DBMS), and development tools. These tools assist in transforming database models into production models with minimal manual intervention, yielding an error-free model. It guarantees that both the developing database and production database are updated simultaneously, providing accurate data with ongoing updates.

10. Security and Access Management

The access-control system will ensure that all databases adhere to role-based security protocols. This will prevent unauthorized individuals from accessing or altering the data model. Employing techniques like encryption, Multi-Factor Authentication (MFA), and authorization methods such as usernames & passwords will secure sensitive information within a data model. This aligns with industry security policies like HIPAA, GDPR, and PCI-DSS, ensuring adherence to best security practices for data modeling.

Data Modeling Tools in DBMS

1. Entity Relationship (ER)/Studio

Developed by IDERA, this robust data modeling tool is designed for the creation, documentation, or management of a database. It supports conceptual, logical, and physical data modeling, suitable for generating an ER diagram and establishing the schema for the database. It boasts extensive features that enable users to collaborate on the same model, with version control included. It includes functions like data dictionary management, impact analysis, and metadata modeling for large database designs. It is compatible with multiple DBMS platforms, including SQL Server, Oracle, MySQL, PostgreSQL, etc.

Example: In a bank, ER/Studio is employed to design a secure and scalable database capable of efficiently managing customer transactions. It ensures data integrity while adhering to compliance regulations such as GDPR.

2. IBM InfoSphere Data Architect

IBM has developed this all-encompassing data modeling tool for large-scale database architectures. It supports data modeling for relational, NoSQL, and big data platforms, compatible with contemporary technologies. Its advanced features include data integration, business reference support, and metadata management. It enables dynamic schema generation, reverse engineering, and governance of data to uphold integrity. It integrates seamlessly with IBM’s other data management tools and analytics solutions, making it excellent for extensive database management enterprises.

Example: In a hospital, InfoSphere Data Architect is utilized to create a data model that maintains consistent patient records across all branches globally. IBM InfoSphere ensures proper interaction between databases while complying with HIPAA regulations.

3. Microsoft Visio Tool

The Microsoft Visio tool is effectively used for designing ER diagrams. It possesses data modeling capabilities that enable users to visually construct a database structure and link it to a live database server for real-time automatic updates. It is not solely utilized for designing a data model but also for conceptual and logical data modeling, as Microsoft Office facilitates connection among developers across a single server, allowing for updates and collaborative work on projects.

It integrates with Microsoft servers for seamless collaboration with other relational databases for schema creation and reverse engineering. Microsoft’s offering of Microsoft 365 provides cloud-based connectivity, enabling teams to work collaboratively on diagrams.

Example: A software development team within a company might use Visio to create an ER diagram in conjunction with their project management system, where entities have hierarchical relationships with the project manager and assigned individuals for a project, prior to implementing it in SQL Server.

4. Oracle SQL Developer Data Modeler

Oracle SQL Developer is a complimentary tool for data modeling specifically tailored for the Oracle database. It supports conceptual, logical, and physical modeling and can also generate SQL scripts based on the model for database construction. Featuring a user-friendly interface, it helps define entities, attributes, relationships, constraints, and indexes. This tool is suited for developers utilizing Oracle database technologies for seamless integration.

Example: An E-Commerce company utilizes a stock management system where the Oracle SQL developer is tasked with creating a data model design to manage orders, transactions, shipping details, stock levels, and user information within their Oracle-based database.

5. SAP PowerDesigner

SAP
“““html

PowerDesigner is a solution utilized by large organizations. It excels in database design, data management, and metadata oversight. It is capable of modeling conceptual, logical, and physical entities, in addition to features like forward and reverse engineering. It can generate an impact analysis for a corporation. SAP provides extensive integration, which is particularly beneficial for sizable enterprises, as it ensures stability and version control, enabling large teams to collaborate effectively on models. It accommodates both relational and NoSQL databases and big data analytics, which are vital for contemporary data infrastructures.

Example: A multinational corporation leverages SAP PowerDesigner to structure and oversee intricate database operations across all SAP ERP systems, ensuring data is consistently relayed to all financial, HR, and supply chain modules.

6. Lucidchart

Lucidchart is a web-based diagramming application that aids users in creating ER diagrams alongside some fundamental data modeling capabilities. It includes a built-in drag-and-drop feature for designing database architectures. It facilitates the virtual creation of the entity relationship. While it isn’t exclusively focused on modeling, it allows for quick, effortless, collaborative designs for teams. It integrates well with platforms such as Google Drive, Confluence, Jira, and Microsoft Office, making remote work efficient and supporting real-time collaboration for agile teams.

Example: A small enterprise, such as a start-up, can utilize Lucidchart to collaboratively architect a database schema for their Software as a Service (SAAS) application, allowing team members to provide suggestions, feedback, and updates in real time.

7. Toad Data Modeler Tools

The Toad Data Modeler, created by Quest Software, is employed to design and manage database schemas. It supports a wide range of databases including relational, NoSQL, Oracle, MySQL, PostgreSQL, SQL Server, and MongoDB. It also features reverse engineering, schema comparison, and the ability to generate SQL scripts based on the created model. The tool boasts a user-friendly interface with drag-and-drop capabilities for simplified modeling. Additional features include impact analysis, automation, and reporting for development and upkeep.

Example: An export company utilizes Toad Data Modeler to design a database for monitoring ships and containers in a warehouse, employing various SQL and NoSQL database systems.

8. DbSchema Tools

DbSchema is a multi-platform tool that caters to relational, NoSQL, and cloud databases. It offers a robust visual interface for designing, documenting, and deploying database schemas. Features such as offline modeling allow users to formulate data models without an internet connection or live database engagement. This tool comes with built-in capabilities for query creation, visual exploration, and schema synchronization. Utilizing cloud technology facilitates team collaboration on schema designs in real-time.

Example: In a gaming company, DbSchema can facilitate team collaboration throughout the design process, allowing for alterations. The database can store player profiles, game progress, and in-game purchases.

How to Select the Appropriate Data Modeling Tool?

When modeling a database, choosing the right data modeling tool is crucial. To achieve an effective model, it is essential to employ specific tools for distinct requirements. The developer must select a suitable tool by evaluating factors such as business needs, technical specifications, and industry standards.

Factors to Evaluate

Several significant factors should be taken into account before developing a data model for businesses.

Industry-Specific Requirements:

• In the healthcare sector, compliance with HIPAA regulations is mandatory when creating a data model that supports the storage of patient records in an EHR system, enabling structured electronic health record storage. Tools like ER/Studio and IBM InfoSphere Data Architect can be employed for efficient data modeling.
• In the finance sector, the need for stringent security and access control measures to safeguard the database necessitates audit trails and adherence to regulations such as GDPR, SOX, and Basel III. Tools like ERwin Data Modeler can be utilized to ensure robust security and governance risk management features.
• In the E-commerce sector, a model capable of managing extensive transactional data for inventory, stock management, and customer interactions is critical. This requires a strong cloud-supported model enabling the sharing of transactional data across the cloud database for easier access and updates; examples of suitable modeling tools include Snowflake and Amazon Redshift.
• In the manufacturing sector, enterprises require supply chain integration, real-time data processing, and impact analysis. Data modeling tools that support IoT and ERP integration, like SAP PowerDesigner, are highly efficient to consider.
• In a telecommunications network, handling large datasets, real-time analysis, and network performance monitoring is essential. The chosen tools must possess strong big data capabilities. Data modeling tools like Apache Cassandra and Vertabelo are appropriate for this purpose.

Database Normalization

The method of organizing data to reduce redundancy and enhance data integrity is termed database normalization. It structures the database according to various normal forms (NF). Each NF aims to eliminate irrelevant or inconsistent data from the database.

Types of Normal Forms in Database Normalization

There are four types of normal forms in database normalization—1NF, 2NF, 3NF, and Boyce-Codd Normal Form (BCNF).

1. 1NF – First Normal Form

1. In the first normal form, all columns within a table must contain atomic values.
2. Each column must consist of only one data type, either numerical or categorical; no mixtures are allowed.
3. Every row must have a unique identifier, known as the primary key.

Error code prior to optimization to 1NF:

A table that contains multiple values within a single column violates the atomicity requirement of 1NF.

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(255),
    ProductsPurchased VARCHAR(255)
);

Explanation: In this scenario, the row holds more than one value (products purchased), which contravenes the atomicity rule and complicates query processing.

Solution to rectify the error:

• The user must segment the multiple values in a row into individual rows.
• Separate rows need to be established for each data type instead of aggregating them in one column.

Example:

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(255)
);

CREATE TABLE OrderDetails (
    OrderID INT,
    Product VARCHAR(255),
    PRIMARY KEY (OrderID, Product),
    FOREIGN KEY (OrderID)

“““html
REFERENCES Orders(OrderID)
);

Clarification: It segregated the multiple values in one row and transformed them into distinct rows for each data type.

• This will guarantee the atomicity.
• Processing queries will become more straightforward. The user can filter the information effortlessly.
• It averts irregular data and multiple values within a single column.

2. 2NF – Second Normal Form

• The second normal form ensures that the table is already formatted in the first normal form.
• It eliminates all partial dependencies within a table.
• This means that every attribute column must entirely rely on the primary key, implying the complete attribute should depend solely on that single primary key in the table.

Error code prior to optimization with 2NF:

In a dining establishment, when a customer places an order online via the restaurant’s website, there will be a record if they ordered more than one food item. To resolve that, the user can apply 2NF.

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(255), 
    CustomerPhone VARCHAR(20)  
);

Solution to rectify the error in 2NF:

• Rather than retrieving all the information in a single column, the user can create distinct columns or tables for each type.
• Can separate the orders table from the customer details to facilitate query processing.

CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    CustomerName VARCHAR(255),
    CustomerPhone VARCHAR(20)
);

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

CREATE TABLE OrderDetails (
    OrderID INT,
    Product VARCHAR(255),
    PRIMARY KEY (OrderID, Product),
    FOREIGN KEY (OrderID) REFERENCES Orders(OrderID)
);

Clarification:

Here, the tables have been created separately to maintain a tidy database. It minimizes data redundancy and enhances the table’s efficiency.

3. 3NF – Third Normal Form

• The third normal form ensures that the table is already in the second normal form.
• It guarantees that an attribute is not dependent on another attribute within the table, which refers to transitive dependencies.

Error before optimization to the second normal form:

• In the customer table, when orders are placed, the system obtains the zip codes for delivery, along with corresponding city details. However, occasionally, both attributes may merge and complicate the table.

CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    CustomerName VARCHAR(255),
    CustomerPhone VARCHAR(20),
    CustomerZIP INT,
    CustomerCity VARCHAR(255) -- Transitive dependency
);

Clarification:

Two attributes must not depend on one another. Instead, they should rely on their respective primary key. In this case, they depend on the primary key of customer ID.

Solution to resolve the error in 3NF:

• The user should establish a separate table with ZIP codes as the primary key. This action will decouple the two attribute dependencies.
• Now, 3NF has eliminated transitive dependencies and augmented the table’s efficiency.

CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    CustomerName VARCHAR(255),
    CustomerPhone VARCHAR(20),
    CustomerZIP INT,
    FOREIGN KEY (CustomerZIP) REFERENCES ZIPCodes(CustomerZIP)
);

CREATE TABLE ZIPCodes (
    CustomerZIP INT PRIMARY KEY,
    CustomerCity VARCHAR(255)
);

Clarification: The third normal form has removed redundant data from the table and facilitated data integrity.

BCNF – Boyce-Codd Normal Form

• In Boyce-Codd normal form, the table must already conform to the third normal form.
• Every determinant (a column capable of uniquely identifying each row) must function as a candidate key.

Error before optimization in BCNF:

• Let’s examine a college database where a professor instructs a course in a classroom. However, the room is allocated to teach only one course, while the professor might teach multiple courses.
• In this case, the Room Number is essentially the Course ID, but it is not established as the candidate key. This situation leads to a dependency error.

CREATE TABLE CourseAssignments (
    ProfessorID INT,
    CourseID INT,
    RoomNumber INT,
    PRIMARY KEY (ProfessorID, CourseID)
);

Clarification:

• The RoomNumber is viewed as corresponding to CourseID. The room needs to dictate the courses to be taught in that particular class within the table.
• However, the RoomNumber does not qualify as a candidate key, which violates BCNF regulations.

Solution to rectify the error in BCNF:

• Establish two distinct tables to partition the data according to the keys.
• The Courses in the table will depend on CourseID and ProfessorID.
• RoomAssignments will depend on CourseID and RoomNumber.

CREATE TABLE Professors (
    ProfessorID INT PRIMARY KEY,
    ProfessorName VARCHAR(255)
);

CREATE TABLE Courses (
    CourseID INT PRIMARY KEY,
    CourseName VARCHAR(255),
    ProfessorID INT,
    FOREIGN KEY (ProfessorID) REFERENCES Professors(ProfessorID)
);

CREATE TABLE RoomAssignments (
    RoomNumber INT PRIMARY KEY,
    CourseID INT,
    FOREIGN KEY (CourseID) REFERENCES Courses(CourseID)
);

Clarification:

• The BCNF has eradicated anomalies (irregular data) that are reliant on functional dependencies.
• It ensures that all determinants serve as candidate keys within the table.
• It prevents inconsistencies in data.

Summary of Normal Forms in DBMS

Indexing in DBMS

Indexing within a database management system (DBMS) is a strategy in data structuring utilized for the rapid retrieval of records from a database table. The functions will scan the full database to acquire data, but if an index is present in the table or database, fetching the data will be more efficient, as the function
“““html

will retrieve the value relying solely on the index value.

Significance of Indexing in Data Modeling

Indexing each database is crucial for several reasons.

• The index will accelerate the process of retrieving data, making it more efficient as the scanning duration decreases thanks to the indexed value retrieval.
• It employs a sorting and grouping technique using subqueries such as ORDER BY and GROUP BY. This method arranges and groups the table, allowing for easier retrieval by index.
• If a user wishes to connect multiple tables, they can indicate the index value of the foreign key, expediting the table joining process by simply specifying their index values.
• It minimizes disk input and output operations by shortening the time needed to scan extensive datasets, thereby enhancing efficiency.

Kinds of Indexes in DBMS

There are three varieties of indexes in Database Management Systems (DBMS): primary indexes, clustered indexes, and non-clustered indexes.

1. Primary Index

A primary index is associated with the primary key of a table. The index applies based on the primary key, which represents the unique identity of each row and is stored after being sorted by the primary key.

Characteristics:

• The index is established once the user defines a primary key for the table.
• This index guarantees no duplicate values exist in the table, as each index is distinct.
• The primary index assists users in easily retrieving or searching for records using the primary key.

Example:

CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY,
    Name VARCHAR(100),
    Salary DECIMAL(10,2)
);

Clarification: The primary key here is the Employee ID, which automatically generates the primary index for Employee ID.

Searching the Primary Key Index:

• The database organizes all data in a structured manner.
• Thus, when a user needs to search for an employee by specifying EmployeeID = 02, the database will locate it using the index value rather than scanning the entire database.

2. Clustered Index

The Clustered index in the database determines how values are physically stored. When a table has a clustered index, the rows stored on disk correspond to the index values.

Characteristics:

• Data within the rows is organized in the database according to the index value.
• The arrangement of records occurs physically at the disk storage level.
• Only one clustered index is permissible per table.
• It is highly effective when users employ it with range-based queries such as finding values using (BETWEEN, <, >), etc.

Example:

CREATE CLUSTERED INDEX idx_emp_name ON Employees(Name);

Clarification: The database first organizes the records physically based on their index values. If a user wishes to search or retrieve specific records, they could specify, for instance, WHERE name = ‘Priya.’ The database will look for the particular index in a row instead of scanning the entire table.

The data is physically stored in an organized format, which is advantageous when users need to retrieve records within ranges, such as WHERE Name BETWEEN ‘F’ AND ‘P’, allowing for easy retrieval.

3. Non-Clustered Index

A Non-Clustered Index is a type of index that keeps the sorted index entries in a distinct structure from the physical arrangement, without modifying any records within the physical structure.

Characteristics:

• The index maintains column values separately in a sorted format.
• In contrast to a clustered index, multiple non-clustered indexes can exist in a table.
• This does not alter anything within the physical storage, but it includes a reference to the actual rows.
• It is highly efficient when users need to search without impacting the structure.

Example:

CREATE NONCLUSTERED INDEX idx_salary ON Employees(Salary);

Clarification: The index stores the salary values in a structure that is sorted according to the actual records.

If a user searches WHERE Salary = 28934, it will quickly locate the record from the relevant row. This index will avoid scanning the entire table.

Applications:

A common instance of daily use is as follows:

SELECT * FROM Employees WHERE Salary > 74000;

This will return the salaries and details of employees whose earnings exceed 74,000.

Contrast Between Clustered and Non-Clustered Indexes

Instances of Indexes in DBMS

Scenario 1: To locate an employee’s details by using their ID number.

CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY, 
    Name VARCHAR(100) NOT NULL,
    Salary DECIMAL(10,2) NOT NULL
);
INSERT INTO Employees (EmployeeID, Name, Salary) VALUES
(103, 'Mary', 24900),
(105, 'Kani', 45000),
(101, 'Karan', 80000),
(107, 'Pavi', 59600);
CREATE NONCLUSTERED INDEX idx_salary ON Employees(Salary);
SELECT EmployeeID, LEFT(Name, 5) AS Name, Salary FROM Employees WHERE EmployeeID = 103;

Output:

Clarification: The indexes retrieved the employee details for the employee with EmployeeID = 103.

Scenario 2: To obtain the customer details for names starting with ‘N.’

CREATE TABLE Orders
``````sql
(
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(100) NOT NULL,
    OrderDate DATE NOT NULL,
    TotalAmount DECIMAL(10,2) NOT NULL
);
INSERT INTO Orders (OrderID, CustomerName, OrderDate, TotalAmount) VALUES
(1, 'Naveen', '2024-01-10', 250.50),
(2, 'Praveen', '2024-01-15', 300.00),
(6, 'Tarun', '2024-03-10', 350.25);
CREATE NONCLUSTERED INDEX idx_customer_orderdate ON Orders(CustomerName, OrderDate);
SELECT OrderID, LEFT(CustomerName, 7) AS CustomerName, OrderDate, TotalAmount 
FROM Orders WHERE CustomerName LIKE 'N%' ORDER BY OrderDate DESC;

Output:

Explanation: The details of the customer whose name begins with N along with their order information have been retrieved in the Clustered Index.

Conclusion

Systematically organizing and structuring data ensures efficiency, reliability, and integrity while minimizing redundancy in a DBMS. Data modeling occurs within conceptual, logical, and physical frameworks to establish databases. Various data modeling tools, such as ER/Studio, Oracle SQL Developer, and SAP PowerDesigner, assist in visualizing and refining the design of data models. Techniques like normalization (1NF, 2NF, 3NF, and BCNF) will help eliminate anomalies, while indexing methods—including primary index, clustered index, and non-clustered index—enhance query performance. When implemented effectively, data modeling yields a database that is both scalable and secure. In this article, you have acquired insights regarding data modeling in database management systems (DBMS).

To further your understanding of SQL, consider enrolling in the SQL Course, and explore the SQL Interview Questions curated by industry professionals.

Data Modeling in DBMS – FAQs

The post Data Models in DBMS appeared first on Intellipaat Blog.

“`